SpeechDx is a multi-task benchmark with 12 datasets and 27 tasks across health conditions, structured by conceptualization, formulation, and articulation stages, showing that no current audio encoder generalizes reliably.
hub
SpeechBrain: A general- purpose speech toolkit
31 Pith papers cite this work. Polarity classification is still indexing.
31
Pith papers citing it
abstract
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, flexible, user-friendly, and well-documented. This paper describes the core architecture designed to support several tasks of common interest, allowing users to naturally conceive, compare and share novel speech processing pipelines. SpeechBrain achieves competitive or state-of-the-art performance in a wide range of speech benchmarks. It also provides training recipes, pretrained models, and inference scripts for popular speech datasets, as well as tutorials which allow anyone with basic Python proficiency to familiarize themselves with speech technologies.
hub tools
representative citing papers
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
HiCoDiT generates speech from video by conditioning low-level RVQ tokens on speaker identity and high-level tokens on facial expressions via a dual-scale normalized diffusion transformer.
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
DASB is a new benchmark for discrete audio tokens showing semantic tokens outperform acoustic ones but discrete representations remain less robust than continuous features across domains.
LuxEmo is a new 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories, created via semi-automatic curation from RTL broadcasts and used to benchmark five TTS systems.
Dual-reference benchmarking on atypical stuttered speech reveals disparities in ASR model performance and rankings between verbatim and intended transcriptions.
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
SRD provides a threshold-independent, representation-level privacy assessment for voice anonymization that reveals system weaknesses not detected by equal error rate evaluation.
A paradigm converts any metric into a minimum edit distance equivalent to interpret ASR errors in terms of human perception and severity.
Chain-of-Details (CoD) is a cascaded TTS method that explicitly models temporal coarse-to-fine dynamics with a shared decoder, achieving competitive performance using significantly fewer parameters.
Common-word acoustic cues and bias-word position prediction in speech LLMs cut rare-word transcription errors by 16.3% versus baselines, including out-of-domain cases.
FAC-FACodec is a controllable zero-shot foreign accent conversion framework using a factorized speech codec that adds an explicit parameter for adjusting pronunciation-level accent modification strength.
ProPS uses a mixture density network conditioned on SBERT text embeddings to generate Gaussian mixture models over speaker x-vectors from natural language profile descriptions.
Mamba matches Conformer accuracy for ASR in seven South African languages with lower compute, multilingual training improves results, and language embeddings aid cross-corpus robustness but do not capture typological similarity.
LISE decomposes pretrained speaker embeddings into components that preserve ASV performance with negligible EER degradation and enable listeners to distinguish speakers at 83.9% accuracy.
Event-driven SpeechMamba with FATReLU reaches over 60% sparsity and spiking version over 70% sparsity with <1% accuracy drop while cutting parameters 30% versus prior SNNs, plus a new cycle-accurate simulator.
A phoneme-based syllabic decoder for Vietnamese ASR outperforms larger-vocabulary baselines like PhoWhisper on standard and multi-dialect benchmarks while using a compact inventory.
Introduces INSV-A automated screening benchmark for Pashto TTS systems reporting WER, script fidelity, and LID results across five systems on FLEURS and Common Voice prompts.
HATS supplies human side-by-side preference judgments on ASR transcripts to measure correlation with lexical and embedding-based evaluation metrics.
A toolkit flags spurious correlations in speech datasets by checking if non-speech regions predict the target class better than chance.
DeepFense supplies a unified toolkit and large-scale benchmarks showing that pre-trained front-end feature extractors drive most performance differences while top models exhibit strong biases by audio quality, speaker gender, and language.
Prioritizing longest utterances in SSL speech pre-training data outperforms random or diversity-based sampling for ASR performance while using half the data volume.
ESPnet3 introduces a new modular architecture with DataOrganizer and sharding to cut training time and simplify model integration for speech research.
citing papers explorer
-
SpeechDx: A Multi-Task Benchmark for Clinical Speech AI
SpeechDx is a multi-task benchmark with 12 datasets and 27 tasks across health conditions, structured by conceptualization, formulation, and articulation stages, showing that no current audio encoder generalizes reliably.