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arxiv: 2604.25938 · v1 · submitted 2026-04-16 · 💻 cs.SD · cs.AI· eess.AS

Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model

Adelekun Oluwademilade , Ademola Adedamola , Abiola Abdulhakeem , Akinpelu Azeezat , Eraiyetan Israel , Omotosho Oluwadunsin , Ibenye Ikechukwu , Ayuba Muhammad
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Olusanya Olamide Kamorudeen Amuda
This is my paper

Pith reviewed 2026-05-10 09:55 UTC · model grok-4.3

classification 💻 cs.SD cs.AIeess.AS
keywords speech emotion recognitionMFCC featuresLSTMdeep learningTESS datasetemotion classificationhuman-computer interaction
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The pith

MFCC features fed into an LSTM network recognize speech emotions at 99 percent accuracy.

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

This paper develops a speech emotion recognition system by extracting Mel-Frequency Cepstral Coefficients from audio signals and processing them with a Long Short-Term Memory deep learning model. The system is evaluated on the Toronto Emotional Speech Set, achieving 99 percent accuracy across multiple emotion classes, which exceeds the 98 percent from a support vector machine baseline. A sympathetic reader would care because accurate emotion detection from voice could enable more responsive human-computer interactions and applications in areas like mental health monitoring. The work establishes that LSTM networks can effectively learn the temporal patterns in speech modified by emotional states.

Core claim

The authors show that transforming pre-processed speech signals from the TESS dataset into MFCC features and inputting them to an LSTM model enables the network to learn long-term sequential dependencies, resulting in highly accurate classification of emotions such as those present in the dataset. This LSTM-based classifier outperforms a classical SVM with RBF kernel, which reaches only 98 percent accuracy. The results confirm that such architectures are suitable for the task of speech emotion recognition.

What carries the argument

Mel-Frequency Cepstral Coefficients extracted from speech signals, used as input to a Long Short-Term Memory neural network for classifying emotional states.

If this is right

  • The MFCC-LSTM approach captures emotional patterns in speech effectively.
  • It delivers highly realistic classifications for all selected emotion classes.
  • The LSTM model can be applied to address speech emotion recognition tasks.
  • Potential uses include virtual assistants and mental health surveillance systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the accuracy holds on datasets beyond TESS, the system could handle real-world variations in speakers and conditions more reliably.
  • Combining this audio-based method with visual cues might create more robust multimodal emotion recognition.
  • Further testing on diverse recording environments would clarify the model's practical limits.

Load-bearing premise

The model's high performance on the TESS dataset means it has learned generalizable features of emotional speech rather than patterns unique to that collection of recordings.

What would settle it

Testing the model on an independent speech dataset recorded under different conditions or with new speakers and finding significantly lower accuracy would disprove the claim of effective emotion pattern capture.

read the original abstract

Speech Emotion Recognition (SER) is the use of machines to detect the emotional state of humans based on the speech, which is gaining importance in natural human-computer interaction. Speech is a very valuable source of information, as emotions modify the patterns of speech; pitch, energy and even timing. Nonetheless, SER is not an easy task because speakers are not constant, and situations vary when recording and the sound similarity between specific feelings. In this work, the author introduces a speech emotion recognition system relying on the Mel-Frequency Cepstral Coefficient and Long Short-Term Memory (LSTM) neural network, as a feature extraction method. The Toronto Emotional Speech Set (TESS) speech signal was pre-processed, and transformed into MFCC features to understand the important aspects in terms of time. The resultant features were then introduced to LSTM model, which is able to learn long term features of sequential audio data. The trained model was measured over several emotion classes occurring in the dataset. As seen in the results of experiments, the proposed MFCC-LSTM approach succeeds in capturing the patterns of emotions in speech and provides highly realistic classifications in all the chosen emotion classifications. This study presents a speech emotion recognition system using Mel-Frequency Cepstral Coefficients (MFCCs) as features and a deep learning LSTM classifier. A Support Vector Machine (SVM) with an RBF kernel served as a classical baseline, achieving 98% accuracy, against which the proposed LSTM model, achieving 99% accuracy, was validated. Overall, it is possible to confirm that LSTM-based architectures can be used to address the task of speech emotion recognition. Actual applications of the proposed system may be virtual assistants and mental health surveillance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents a speech emotion recognition (SER) system that extracts Mel-Frequency Cepstral Coefficients (MFCC) from the Toronto Emotional Speech Set (TESS) dataset and feeds them into an LSTM classifier, reporting 99% accuracy versus 98% for an SVM baseline with RBF kernel. The abstract claims the MFCC-LSTM approach captures emotion patterns and yields highly realistic classifications across the dataset's emotion classes.

Significance. If the 99% accuracy were obtained under a speaker-independent protocol with proper cross-validation, the result would provide empirical evidence that LSTM networks can model sequential dependencies in MFCC features for SER on TESS. The inclusion of an SVM baseline offers a minimal but useful point of comparison. However, the current lack of methodological detail on evaluation prevents any assessment of whether the performance reflects genuine emotion modeling or dataset artifacts.

major comments (1)
  1. [Abstract and Results] Abstract and Results: The central claim that the LSTM model 'succeeds in capturing the patterns of emotions in speech' and achieves '99% accuracy' is unsupported because the manuscript supplies no information on the train-test split, cross-validation scheme, hyperparameter selection, or error analysis. TESS contains utterances from only two speakers; without an explicit speaker-disjoint (leave-one-speaker-out) partition, the reported accuracy cannot be interpreted as evidence of emotion-specific generalization rather than speaker or recording artifacts.
minor comments (2)
  1. [Abstract] Abstract: The phrasing 'the author introduces' is inconsistent with standard academic style; 'this work introduces' or 'we introduce' would be preferable. The clause 'the sound similarity between specific feelings' is unclear and should be rephrased for precision.
  2. [Methods] Overall: No architecture diagram, layer dimensions, or training hyperparameters for the LSTM are provided, making the model non-reproducible from the text alone.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thorough review and constructive criticism. We respond to the major comment as follows and will incorporate the suggested improvements in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract and Results] Abstract and Results: The central claim that the LSTM model 'succeeds in capturing the patterns of emotions in speech' and achieves '99% accuracy' is unsupported because the manuscript supplies no information on the train-test split, cross-validation scheme, hyperparameter selection, or error analysis. TESS contains utterances from only two speakers; without an explicit speaker-disjoint (leave-one-speaker-out) partition, the reported accuracy cannot be interpreted as evidence of emotion-specific generalization rather than speaker or recording artifacts.

    Authors: We acknowledge the validity of this comment. The submitted manuscript indeed omitted key details about the evaluation methodology, which is a significant shortcoming. In the revised version, we will add a detailed 'Experimental Setup' section that specifies the train-test split procedure, the cross-validation scheme employed, the hyperparameter selection process, and an error analysis including confusion matrices. Furthermore, we will explicitly discuss the dataset's composition (only two speakers) and the implications for generalization. We will clarify the nature of the split used and include additional analysis to support the claims about capturing emotion patterns. The abstract will be revised to more accurately reflect the supported claims. revision: yes

Circularity Check

0 steps flagged

No circularity: standard empirical ML pipeline with no derivations

full rationale

The paper presents an empirical speech emotion recognition system that extracts MFCC features from the TESS dataset and trains an LSTM classifier, reporting 99% accuracy against an SVM baseline. No mathematical derivations, equations, first-principles results, or predictions are claimed that could reduce to fitted parameters or self-citations by construction. The accuracy metric is a direct experimental outcome of model training on the provided data split, with no load-bearing self-referential steps or ansatzes imported via citation. This is a typical applied ML study whose central claim rests on empirical results rather than any closed derivation chain.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that MFCC coefficients adequately encode emotional information in speech and that LSTM can learn temporal dependencies from the TESS recordings; no new entities are postulated and no free parameters are explicitly fitted beyond standard neural-network training.

free parameters (1)
  • LSTM architecture hyperparameters
    Number of layers, hidden units, learning rate, and sequence length are chosen during training but not reported in the abstract.
axioms (1)
  • domain assumption MFCC features capture the emotional content of speech signals
    Invoked when the paper states that MFCCs are used to understand important aspects in terms of time.

pith-pipeline@v0.9.0 · 5668 in / 1283 out tokens · 39800 ms · 2026-05-10T09:55:06.221898+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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