Pith. sign in

REVIEW 3 major objections 8 minor 55 references

Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets

T0 review · 3 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that restructuring a Time Delay Neural Network (TDNN) into a funnel shape, adding two 1x1 intermediate layers, and grid-searching temporal context and dilation lifts spoken language recognition to 97% accuracy across ten…

desk verdict A readable but incremental TDNN/x-vector engineering study whose headline 97% accuracy is a validation-set number, with no test-set accuracy, speaker-disjoint splits, or SOTA comparison to back it up. read the letter →

arxiv 2501.11065 v1 pith:SLX3MEWO submitted 2025-01-19 cs.SD cs.AIcs.LGeess.AS

Or Haim Anidjar , Roi Yozevitch This is my paper · ORCID
classification cs.SDcs.AIcs.LGeess.AS
keywords LanguageIdentificationSpokenRecognitionX-vectorsMultilingualDatasetsTDNNTimeDelayNeuralNetworkCommonVoiceDataAugmentation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a spoken language recognition system based on x-vector embeddings can reach 97% accuracy on ten languages from the Common Voice corpus by restructuring the Time Delay Neural Network (TDNN). The key changes are adding two 1x1 TDNN intermediate layers, reshaping the TDNN layers into a progressively narrowing "funnel" that reduces dimensionality, and grid-searching the context size and dilation per layer. These modifications lift validation accuracy from 0.54 in the baseline architecture to 0.969 in the final model, with data augmentation adding robustness. If the result holds, it suggests that architectural changes to embedding networks matter as much as more data for language identification.

What carries the argument

The central object is the x-vector Time Delay Neural Network with a statistical pooling layer that aggregates frame-level features over the full utterance. The paper's modifications are (1) two 1x1 TDNN intermediate layers, which apply nonlinear transforms to the current frame only; (2) a funnel-shaped width schedule, 1280 down to 256 neurons per layer, which progressively reduces dimensionality and acts as an information bottleneck; and (3) a grid search over context size and dilation in the first three TDNN layers. The statistical pooling layer is what lets the network represent language-level characteristics rather than frame-level acoustics; the funnel and 1x1 layers are what the paper shows improve discrimination.

What would settle it

Re-run the final model on a speaker-disjoint partition of the same ten Common Voice languages, ensuring no speaker appears in more than one split; if accuracy drops far below 97%, the original number reflects speaker identity leakage rather than language recognition.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a standard x-vector TDNN can be converted into a high-accuracy language identifier by a specific set of architectural interventions: 1x1 TDNN layers inserted as intermediate nonlinear transforms, a funnel-shaped width schedule (1280 down to 256 neurons) that acts as an information bottleneck, and per-layer grid-searched temporal contexts and dilations. The final model, trained on balanced ten-language data from Common Voice with speed, pitch, and noise augmentation, achieves 0.969 validation accuracy, which the abstract reports as a 97% language recognition rate. The authors attribute the gains to better capture of long-term language characteristics through the temporal pooling layer combined with the funnel's hierarchical feature extraction and regularization.

Load-bearing premise

The result stands on the assumption that the training, validation, and test segments do not share the same speakers; the paper never states that the Common Voice split is speaker-disjoint, so part of the 97% could come from recognizing voices rather than languages.

Editorial extensions

If this is right

  • The integrated architecture (grid search + 1x1 layers + funnel + augmentation) reaches 0.969 validation accuracy, up from 0.54 for the unmodified x-vector baseline.
  • Each modification contributes independently: grid search 0.85, intermediate layers 0.79, funnel structure 0.92, integrated model 0.95, and final augmented model 0.969.
  • The funnel structure alone provides the largest single improvement, suggesting that dimensionality reduction and bottleneck regularization are effective for language identification.
  • With less data than typical x-vector baselines, the modified network still outperforms the baseline, pointing to architecture rather than dataset size as the main driver.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If speaker-disjoint re-evaluation confirms the accuracy, the same funnel-plus-1x1 recipe could be transferred to other embedding tasks such as speaker verification or emotion recognition, where long-term temporal pooling already plays a role.
  • The paper does not report per-language accuracy breakdowns beyond a comment that Spanish outperformed Russian; a per-language analysis would reveal whether the 97% is evenly distributed or driven by a few easy languages.
  • A practical extension would test the model on unseen languages or code-switched speech, where the paper itself notes remaining challenges; the architecture would need to handle mixed-language segments.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. The paper proposes an enhanced spoken language recognition (SLR) system built on an x-vector/time-delay neural network (TDNN) architecture, trained on ten languages from the Common Voice corpus. The claimed contributions are three architectural modifications: tuning TDNN context size and dilation via grid search, inserting 1x1 TDNN intermediate layers, and restructuring the network into a funnel shape. A final model with data augmentation is reported to achieve 97% accuracy. The experimental section compares incremental modifications on a validation set, with the final model reaching 0.969, and includes confusion matrices for validation and test sets. The paper discusses limitations such as linguistic ambiguity, code-switching, and data scarcity, and proposes future work on unverified data and speaker diarization.

Significance. If the reported 97% accuracy were a robust, externally validated result, the paper would offer a modest but useful contribution to the SLR literature, particularly for low-resource languages. The architectural description is clear, and the incremental ablation (Table 7) is a reasonable way to isolate the effect of each modification. However, the central claim is currently supported only by a validation-set number, the test-set result is acknowledged in the paper to show potential overfitting, and no speaker-disjoint split protocol is described. These issues place the entire reported performance in question. The paper does not ship code, data, or a comparison with existing SLR systems under a common protocol, so its practical value is limited at this stage.

major comments (3)
  1. [Section 5, Table 7, Figures 3-4] The abstract and Section 7 claim a 97% accuracy rate, but the only number supporting this is the validation accuracy of 0.969 in Table 7. The text in Section 5 explicitly states that the test confusion matrix (Figure 4) indicates 'potential overfitting compared to validation results,' yet no numeric test accuracy or test loss is reported anywhere. Because the model's hyperparameters were selected using validation data, the 0.969 figure is a fitted value rather than an estimate of generalization to unseen data. Report the test-set accuracy, provide error bars or repeated-run statistics, and clearly state which number supports the headline claim.
  2. [Sections 3.5 and 3.6] No speaker-disjoint partitioning protocol is described. The dataset is drawn from Common Voice, which contains multiple utterances per speaker, and an x-vector model can potentially discriminate languages by exploiting speaker identity if the same speaker appears in both training and validation/test partitions. The paper itself acknowledges the risk of the model focusing on speakers in Section 5, but the data-preprocessing sections only mention mp3-to-wav conversion, dead-segment removal, and class balancing. Specify exactly how the train/validation/test splits were constructed, including whether any speaker appears in more than one partition, and report the degree of speaker overlap.
  3. [Section 4.2.1] The grid search over TDNN context size and dilation is performed separately for each of the first three layers, with a limited number of epochs per layer, and the text concedes that the identified values 'may not be the absolute optimal.' This greedy, per-layer selection is not a joint optimization, and it is performed on the same validation set that is later used to report the final accuracy. The improvements attributed to 'grid search' in Table 7 are therefore not independent of the evaluation procedure. Provide a joint search budget, or at minimum, report the validation accuracy on a held-out split that was not used for hyperparameter selection.
minor comments (8)
  1. [Multiple sections] The accuracy claims are inconsistent: the abstract and Section 7 say 97%, Section 4.3 says 95%, and Table 7 reports 0.969. Please reconcile these numbers and ensure the same value is used throughout.
  2. [Section 1.2] The phrase 'Vector-X technique' appears to be a misnomer; the paper otherwise discusses 'x-vector' embeddings. Please correct the terminology for consistency.
  3. [Table 1] The abbreviation table lists 'DNN' twice, and the second entry is spelled 'Depp Neural Network.' This should be fixed.
  4. [Section 2.1] The text states 'Table 1 provides a comparison' of i-vector, x-vector, and d-vector, but the relevant table is Table 2.
  5. [References] References [26] and [34] appear to be the same work ('Stacked Long-Term TDNN for Spoken Language Recognition'), with inconsistent author attribution. Please merge or correct.
  6. [Section 4.2.1] The text refers to 'Temporal Convolutional Neural Network (TDNN)' but TDNN is an abbreviation for 'Time Delay Neural Network.'
  7. [Section 3.2] There is a typo in the opening sentence: 'n our research' should be 'In our research.'
  8. [Tables 3-5] Several dataset statistics appear implausible: French shows 12 recorded hours but 15 validated hours, Farsi 2 vs 4, and Chinese 5 vs 95. If these are not typos, please clarify how validated hours can exceed recorded hours; if they are typos, correct them.

Circularity Check

1 steps flagged · score 6.0 of 10

The 97% headline is the best validation-set accuracy after grid search; test-set accuracy is never reported and is admitted to show overfitting.

  1. fitted input called prediction [Abstract; Section 4.2.1 (grid search); Section 5 / Table 7]
    "Table 7: Comparative Analysis of Model Improvements on Validation Dataset. ... Final Model 0.969. ... A grid search was conducted to optimize the dilation and context size hyperparameters in TDNN layers... The optimal hyperparameters identified were: Layer 1 with a context size of 3 and dilation of 2, achieving an accuracy of 0.72 and a loss of 0.88. ... Figures depicting the confusion matrices for the final model on both validation and test datasets are included, demonstrating the model’s effectiveness and potential overfitting issues, respectively."

    The model's hyperparameters and architecture choices were selected using accuracy on the validation split, and Table 7 explicitly reports all compared accuracies 'on Validation Dataset.' The abstract's headline 'achieving a 97% accuracy rate' and the conclusion's 'achieved an impressive accuracy of 97%' therefore refer to the same validation split used for model selection. No numeric test-set accuracy is given; the test confusion matrix is described as 'indicating potential overfitting compared to validation results.' The advertised number is thus the selected maximum of a search over that split, not an independent prediction on unseen data. Reporting this fitted validation score as 'accuracy in language recognition' makes the central claim a fitted value presented as a prediction.

full rationale

The paper's central claim is a validation-set number obtained after a grid search over the same data split. The test-set confusion matrix is admitted to show overfitting, and no test accuracy is reported. Additionally, the paper does not state that train/validation/test splits are speaker-disjoint, so the validation score may partly reflect speaker identity. This is not a self-contained derivation or an external benchmark; the reported 97% is a fitted result rather than a validated prediction. The circularity is real and load-bearing, but the paper does include an independent baseline (x-vector baseline 0.54) and reports multiple model variants, so the central claim is partially forced rather than fully definitional. Score 6.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

The central claim depends on the hyperparameters chosen by grid search, the hand-picked funnel widths, and the unstated speaker-disjoint split. No new physical entities are introduced.

free parameters (3)
  • TDNN layer context/dilation settings = Layer1: context 3, dilation 2; Layer2: context 5, dilation 2; Layer3: context 2, dilation 1
    Chosen by a layer-wise grid search on the validation set (Section 4.2.1). These settings directly define the architecture that produces the reported accuracy.
  • Funnel layer widths = 1280,1280,1024,1024,768,512,256,512,512
    Selected by the authors to fit their dataset size; Section 4.2.3 says the sizes were adapted differently from the suggested fourth root of the data size without a stated rule.
  • Augmentation parameters (speed, pitch, noise) = not reported
    Section 3.7 lists augmentation types but gives no magnitudes or probability schedules, so they are effectively free and unreported.
assumptions (2)
  • domain assumption Common Voice validated segments have correct language labels
    The dataset section relies on community validation for label correctness (Section 3.3), but no verification is performed in this paper.
  • domain assumption Evaluation is speaker-independent
    The paper does not state that train and test speakers are disjoint; Sections 3.5 and 3.6 describe balancing and preprocessing but not the split protocol. If speakers overlap, the accuracy can reflect speaker identity rather than language.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets." pith.science (2026). https://pith.science/paper/SLX3MEWO

@misc{pith2026250111065,
  author       = {Pith},
  title        = {Pith review of: Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SLX3MEWO}},
  note         = {Machine review of arXiv:2501.11065}
}
read the original abstract

In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting ten languages across Indo-European, Semitic, and East Asian families. The major innovation involved optimizing the architecture of Time Delay Neural Networks. We introduced additional layers and restructured these networks into a funnel shape, enhancing their ability to process complex linguistic patterns. A rigorous grid search determined the optimal settings for these networks, significantly boosting their efficiency in language pattern recognition from audio samples. The model underwent extensive training, including a phase with augmented data, to refine its capabilities. The culmination of these efforts is a highly accurate system, achieving a 97\% accuracy rate in language recognition. This advancement represents a notable contribution to artificial intelligence, specifically in improving the accuracy and efficiency of language processing systems, a critical aspect in the engineering of advanced speech recognition technologies.

Figures

Figures reproduced from arXiv: 2501.11065 by the authors.

Figure 1
Figure 1. Architecture Source Model (Baseline) based on the Spoken Language Recognition using X-vectors. The enhanced architecture of our research model, presented in [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Network topology: The initial phase involves inputting an mp3 file, which undergoes transformation to wav [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Confusion Matrix on Validation Dataset for the Final Model, showing minimal error. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Confusion Matrix on Test Dataset for the Final Model, indicating potential overfitting compared to validation [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 50 canonical work pages

  1. [1]

    Introduction to convolutional neural networks

    Jianxin Wu. Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23):495, 2017

    work page 2017

  2. [2]

    Machine health monitoring with lstm networks

    Rui Zhao, Jinjiang Wang, Ruqiang Yan, and Kezhi Mao. Machine health monitoring with lstm networks. In 2016 10th international conference on sensing technology (ICST), pages 1–6. IEEE, 2016

    work page 2016

  3. [3]

    An integrated cnn-lstm model for bangla lexical sign language recognition

    Nanziba Basnin, Lutfun Nahar, and Mohammad Shahadat Hossain. An integrated cnn-lstm model for bangla lexical sign language recognition. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: Proceedings of TCCE 2020, pages 695–707. Springer, 2020. 12 Enhancing Neural Spoken Language Recognition: An Exploration wit...

    work page 2020

  4. [4]

    Universal attribute charac- terization of spoken languages for automatic spoken language recognition

    Sabato Marco Siniscalchi, Jeremy Reed, Torbjørn Svendsen, and Chin-Hui Lee. Universal attribute charac- terization of spoken languages for automatic spoken language recognition. Computer Speech & Language , 27(1):209–227, 2013

    work page 2013

  5. [5]

    A com- prehensive study on sign language recognition methods

    Nikolas Adaloglou, Theocharis Chatzis, Ilias Papastratis, Andreas Stergioulas, Georgios Th Papadopoulos, Vas- sia Zacharopoulou, George J Xydopoulos, Klimnis Atzakas, Dimitris Papazachariou, and Petros Daras. A com- prehensive study on sign language recognition methods. arXiv preprint arXiv:2007.12530, 2(2), 2020

    arXiv 2007

  6. [6]

    Accelerated profile hmm searches

    Sean R Eddy. Accelerated profile hmm searches. PLoS computational biology, 7(10):e1002195, 2011

    2011

  7. [7]

    Spoken lan- guage recognition using cnn

    Shyamapada Mukherjee, Neeraj Shivam, Astha Gangwal, Lokesh Khaitan, and Amlan Jyoti Das. Spoken lan- guage recognition using cnn. In 2019 International Conference on Information Technology (ICIT), pages 37–41. IEEE, 2019

    work page 2019

  8. [8]

    Graph lstm with context-gated mechanism for spoken language understanding

    Linhao Zhang, Dehong Ma, Xiaodong Zhang, Xiaohui Yan, and Houfeng Wang. Graph lstm with context-gated mechanism for spoken language understanding. InProceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 9539–9546, 2020

    work page 2020

Show all 55 references
  1. [9]

    Self-supervised speaker embeddings

    Themos Stafylakis, Johan Rohdin, Oldrich Plchot, Petr Mizera, and Lukas Burget. Self-supervised speaker embeddings. arXiv preprint arXiv:1904.03486, 2019

    1904 arXiv

  2. [10]

    A stethoscope for drones: Transformers-based methods for uavs acoustic anomaly detection

    Or Haim Anidjar, Alon Barak, Boaz Ben-Moshe, Eyal Hagai, and Sahar Tuvyahu. A stethoscope for drones: Transformers-based methods for uavs acoustic anomaly detection. IEEE Access, 11:33336–33353, 2023

    2023

  3. [11]

    X-vectors: Robust dnn embeddings for speaker recognition

    David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. X-vectors: Robust dnn embeddings for speaker recognition. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 5329–5333. IEEE, 2018

    2018

  4. [12]

    Geoffrey Stewart Morrison. A comparison of procedures for the calculation of forensic likelihood ratios from acoustic–phonetic data: Multivariate kernel density (mvkd) versus gaussian mixture model–universal background model (gmm–ubm). Speech Communication, 53(2):242–256, 2011

    2011

  5. [13]

    End-to-end neural transformer based spoken language understanding

    Martin Radfar, Athanasios Mouchtaris, and Siegfried Kunzmann. End-to-end neural transformer based spoken language understanding. arXiv preprint arXiv:2008.10984, 2020

    2008 arXiv

  6. [14]

    Challenges and issues in adopting speech recognition

    Priyanka Sahu, Mohit Dua, and Ankit Kumar. Challenges and issues in adopting speech recognition. Speech and Language Processing for Human-Machine Communications: Proceedings of CSI 2015, pages 209–215, 2018

    2015

  7. [15]

    Whisper turns stronger: Augmenting wav2vec 2.0 for superior asr in low-resource languages

    Or Haim Anidjar, Revital Marbel, and Roi Yozevitch. Whisper turns stronger: Augmenting wav2vec 2.0 for superior asr in low-resource languages. arXiv preprint arXiv:2501.00425, 2024

    2024 arXiv

  8. [16]

    Harnessing the power of wav2vec2 and cnns for robust speaker identification on the voxceleb and librispeech datasets

    Or Haim Anidjar, Revital Marbel, and Roi Yozevitch. Harnessing the power of wav2vec2 and cnns for robust speaker identification on the voxceleb and librispeech datasets. Expert Systems with Applications, 255:124671, 2024

    2024

  9. [17]

    Extending limited datasets with gan-like self-supervision for sms spam detection

    Or Haim Anidjar, Revital Marbel, Ran Dubin, Amit Dvir, and Chen Hajaj. Extending limited datasets with gan-like self-supervision for sms spam detection. Computers & Security, 145:103998, 2024

    2024

  10. [18]

    Full-covariance ubm and heavy-tailed plda in i-vector speaker verification

    Pavel Mat ˇejka, Ond ˇrej Glembek, Fabio Castaldo, Md Jahangir Alam, Old ˇrich Plchot, Patrick Kenny, Luk ´aˇs Burget, and Jan ˇCernocky. Full-covariance ubm and heavy-tailed plda in i-vector speaker verification. In 2011 IEEE international conference on acoustics, speech and ...

    2011

  11. [19]

    Discriminative neural embedding learning for short-duration text-independent speaker verification

    Shuai Wang, Zili Huang, Yanmin Qian, and Kai Yu. Discriminative neural embedding learning for short-duration text-independent speaker verification. IEEE/ACM Transactions on Audio, Speech, and Language Processing , 27(11):1686–1696, 2019

    2019

  12. [20]

    Multiclass discriminative training of i-vector language recognition

    Alan McCree. Multiclass discriminative training of i-vector language recognition. 2016

    2016

  13. [21]

    Spo- ken language recognition using x-vectors

    David Snyder, Daniel Garcia-Romero, Alan McCree, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. Spo- ken language recognition using x-vectors. In Odyssey, volume 2018, pages 105–111, 2018

    2018

  14. [22]

    Mickael Rouvier and Benoit Favre. Speaker adaptation of dnn-based asr with i-vectors: Does it actually adapt models to speakers? In Fifteenth Annual Conference of the International Speech Communication Association , 2014

    2014

  15. [23]

    Improved conditional generative adversarial net classification for spoken language recognition

    Xiaoxiao Miao, Ian McLoughlin, Shengyu Yao, and Yonghong Yan. Improved conditional generative adversarial net classification for spoken language recognition. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 98–104, 2018

    2018

  16. [24]

    A dcrnn-based ensemble classifier for speech emotion recognition in odia language

    Monorama Swain, Bubai Maji, P Kabisatpathy, and Aurobinda Routray. A dcrnn-based ensemble classifier for speech emotion recognition in odia language. Complex & Intelligent Systems, 8(5):4237–4249, 2022. 13 Enhancing Neural Spoken Language Recognition: An Exploration with Multi...

    2022

  17. [25]

    V oxlingua107: a dataset for spoken language recognition

    J ¨orgen Valk and Tanel Alum¨ae. V oxlingua107: a dataset for spoken language recognition. In2021 IEEE Spoken Language Technology Workshop (SLT), pages 652–658. IEEE, 2021

    2021

  18. [26]

    Stacked long-term tdnn for spoken language recognition

    Alan McCree Daniel Garcia-Romero. Stacked long-term tdnn for spoken language recognition. 2016

    2016

  19. [27]

    V oxlingua107: A dataset for spoken language recognition

    Tanel Alumae Jorgen Valk. V oxlingua107: A dataset for spoken language recognition. 2020

    2020

  20. [28]

    Common voice: A massively-multilingual speech corpus

    Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber. Common voice: A massively-multilingual speech corpus. arXiv preprint arXiv:1912.06670, 2019

    1912 arXiv

  21. [29]

    Self report in clinical and epidemiological studies with non-english speakers: the challenge of language and culture

    Sanja M Hunt and Raj Bhopal. Self report in clinical and epidemiological studies with non-english speakers: the challenge of language and culture. Journal of Epidemiology & Community Health, 58(7):618–622, 2004

    2004

  22. [30]

    Indo-European language and culture: An introduction

    Benjamin W Fortson IV . Indo-European language and culture: An introduction. John Wiley & Sons, 2011

    2011

  23. [31]

    Semitic languages

    Robert Hetzron and Alan S Kaye. Semitic languages. In The world’s major languages, pages 564–572. Rout- ledge, 2009

    2009

  24. [32]

    The topography of certain phonetic and morphological characteristics of south east asian languages

    Eug ´enie JA Henderson. The topography of certain phonetic and morphological characteristics of south east asian languages. Lingua, 15:400–434, 1965

    1965

  25. [33]

    Principles of spoken language recognition

    Chin-Hui Lee. Principles of spoken language recognition. Springer Handbook of Speech Processing , pages 785–796, 2008

    2008

  26. [34]

    Stacked long-term tdnn for spoken language recognition

    Daniel Garcia-Romero and Alan McCree. Stacked long-term tdnn for spoken language recognition. In Inter- speech, pages 3226–3230, 2016

    2016

  27. [35]

    Comparing apples with apples: The importance of clean data

    Erin D Maughan and Lori J Schneider. Comparing apples with apples: The importance of clean data. NASN School Nurse, 34(2):107–108, 2019

    2019

  28. [36]

    The importance of balanced data sets: Analyzing a vehicle trajectory prediction model based on neural networks and distributed representations

    Florian Mirus, Terrence C Stewart, and J ¨org Conradt. The importance of balanced data sets: Analyzing a vehicle trajectory prediction model based on neural networks and distributed representations. In2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. I...

    2020

  29. [37]

    Specaugment: A simple data augmentation method for automatic speech recognition

    Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779, 2019

    1904 arXiv

  30. [38]

    Audio augmentation for speech recogni- tion

    Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur. Audio augmentation for speech recogni- tion. In Sixteenth annual conference of the international speech communication association, 2015

    2015

  31. [39]

    Out domain data augmentation on punjabi children speech recognition using tacotron

    Taniya Hasija, Virender Kadyan, and Kalpna Guleria. Out domain data augmentation on punjabi children speech recognition using tacotron. In Journal of Physics: Conference Series, volume 1950, page 012044. IOP Publish- ing, 2021

    1950

  32. [40]

    Tunability: Importance of hyperparameters of ma- chine learning algorithms

    Philipp Probst, Bernd Bischl, and Anne-Laure Boulesteix. Tunability: Importance of hyperparameters of ma- chine learning algorithms. arXiv preprint arXiv:1802.09596, 2018

    2018 arXiv

  33. [41]

    Character-level language modeling with deeper self-attention

    Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, and Llion Jones. Character-level language modeling with deeper self-attention. In Proceedings of the AAAI conference on artificial intelligence , volume 33, pages 3159–3166, 2019

    2019

  34. [42]

    The importance of bottle- necks in protein networks: correlation with gene essentiality and expression dynamics

    Haiyuan Yu, Philip M Kim, Emmett Sprecher, Valery Trifonov, and Mark Gerstein. The importance of bottle- necks in protein networks: correlation with gene essentiality and expression dynamics. PLoS computational biology, 3(4):e59, 2007

    2007

  35. [43]

    Automatic lyrics transcription using dilated convolutional neural networks with self-attention

    Emir Demirel, Sven Ahlb ¨ack, and Simon Dixon. Automatic lyrics transcription using dilated convolutional neural networks with self-attention. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2020

    2020

  36. [44]

    Densely connected time delay neural network for speaker verification

    Ya-Qi Yu and Wu-Jun Li. Densely connected time delay neural network for speaker verification. In INTER- SPEECH, pages 921–925, 2020

    2020

  37. [45]

    Wire detection using synthetic data and dilated convolutional networks for unmanned aerial vehicles

    Ratnesh Madaan, Daniel Maturana, and Sebastian Scherer. Wire detection using synthetic data and dilated convolutional networks for unmanned aerial vehicles. In2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3487–3494. IEEE, 2017

    2017

  38. [46]

    Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on timit.PloS one, 13(10):e0205355, 2018

    Doroteo T Toledano, Mar ´ıa Pilar Fern´andez-Gallego, and Alicia Lozano-Diez. Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on timit.PloS one, 13(10):e0205355, 2018. 14 Enhancing Neural Spoken Language Recognition: An...

    2018

  39. [47]

    Improving ctc- based acoustic model with very deep residual time-delay neural networks

    Sheng Li, Xugang Lu, Ryoichi Takashima, Peng Shen, Tatsuya Kawahara, and Hisashi Kawai. Improving ctc- based acoustic model with very deep residual time-delay neural networks. In Interspeech, pages 3708–3712, 2018

    2018

  40. [48]

    Deep learning and the information bottleneck principle

    Naftali Tishby and Noga Zaslavsky. Deep learning and the information bottleneck principle. In 2015 ieee information theory workshop (itw), pages 1–5. IEEE, 2015

    2015

  41. [49]

    Speech and multilingual nat- ural language framework for speaker change detection and diarization

    Or Haim Anidjar, Yannick Est `eve, Chen Hajaj, Amit Dvir, and Itshak Lapidot. Speech and multilingual nat- ural language framework for speaker change detection and diarization. Expert Systems with Applications , 213:119238, 2023

    2023

  42. [50]

    Hybrid speech and text analysis methods for speaker change detection

    Or Haim Anidjar, Itshak Lapidot, Chen Hajaj, Amit Dvir, and Issachar Gilad. Hybrid speech and text analysis methods for speaker change detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing , 29:2324–2338, 2021

    2021

  43. [51]

    A thousand words are worth more than one recording: Word-embedding based speaker change detection

    Or Haim Anidjar, Itshak Lapidot, Chen Hajaj, and Amit Dvir. A thousand words are worth more than one recording: Word-embedding based speaker change detection. In Interspeech, pages 3121–3125, 2021

    2021

  44. [52]

    A thousand words are worth more than one recording: Nlp based speaker change point detection

    Or Haim Anidjar, Chen Hajaj, Amit Dvir, and Issachar Gilad. A thousand words are worth more than one recording: Nlp based speaker change point detection. arXiv preprint arXiv:2006.01206, 2020

    2006 arXiv

  45. [53]

    Crossing language identification: Multilingual asr framework based on semantic dataset creation & wav2vec 2.0.Machine Learning with Applications, 13:100489, 2023

    Or Haim Anidjar, Roi Yozevitch, Nerya Bigon, Najeeb Abdalla, Benjamin Myara, and Revital Marbel. Crossing language identification: Multilingual asr framework based on semantic dataset creation & wav2vec 2.0.Machine Learning with Applications, 13:100489, 2023

    2023

  46. [54]

    The use of occurrences of ideas for con- structing and characterizing the design space

    Hernan Casakin, Hadas Sopher, John S Gero, and Or Haim Anidjar. The use of occurrences of ideas for con- structing and characterizing the design space. Proceedings of the Design Society, 4:905–914, 2024

    2024

  47. [55]

    A data-driven nlp approach to analyzing framing and reframing in design protocols

    Hernan Casakin, Hadas Sopher, Or H Anidjar, and John S Gero. A data-driven nlp approach to analyzing framing and reframing in design protocols. 15

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.