Papers with voice quality
Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation (2026.findings-eacl)
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| Challenge: | Large Language Model (LLM) judges are limited to textual content, resulting in expensive and opaque evaluation methods. |
| Approach: | They propose a framework that enables large language model judges to reason over audio cues . they introduce a human chain-of-thought annotation protocol to improve judge diagnostic capability . |
| Outcome: | The proposed framework achieves higher agreement with human raters than ALMs and transcript-only LLM judges while being significantly more cost-effective. |
Revisiting Over-Smoothness in Text to Speech (2022.acl-long)
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| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
InaGVAD : A Challenging French TV and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender Segmentation (2024.lrec-main)
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| Challenge: | InaGVAD is an audio corpus collected from 10 French radio and 18 TV channels categorized into 4 groups: generalist radio, music radio, news TV, and generalist TV. |
| Approach: | They propose to use an audio corpus from 10 French radio and 18 TV channels to represent the acoustic diversity of French audiovisual programs. |
| Outcome: | The proposed system is trained on a single hour of data and achieved competitive results. |
The Role of Creaky Voice in Turn Taking and the Perception of Speaker Stance: Experiments Using Controllable TTS (2024.lrec-main)
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| Challenge: | Recent advances in spontaneous text-to-speech (TTS) have enabled the realistic generation of creaky voice, a voice quality known for its diverse pragmatic and paralinguistic functions. |
| Approach: | They used a creaky voice detection tool and a neural TTS engine to control creaky phonation in a spontaneous speech corpus to investigate the effect of creaky voices on perceived certainty, valence, sarcasm, and turn finality. |
| Outcome: | The proposed model enables the realistic synthesis of creaky voice in perceptual tests without formal training. |