Speech Intelligibility Assessment using Automatic Speech Recognition
Notice bibliographique
Résumé
Hearing loss affects approximately 1.59 billion individuals globally, with projections indicating that nearly 2.5 billion will be impacted by 2050. Despite the increasing prevalence, many individuals delay seeking help, even in well-resourced settings, leading to a significant gap between clinical diagnoses and self-reported difficulties. In Canada, while 19.4% of the population exhibits measurable hearing loss, only 3.7% perceive their impairment, highlighting the need for improved hearing assessment methodologies. A key challenge in hearing impairment is difficulty understanding speech in noise, which traditional pure-tone audiometry fails to capture effectively. This thesis investigates the integration of Automatic Speech Recognition (ASR) models into speech-in-noise (SiN) testing to enhance hearing aid evaluations and enable automated, scalable, and clinically relevant assessments. The research examines ASR models under diverse acoustic conditions, demonstrating their effectiveness in quantifying Signal-to-Noise Ratio (SNR) loss—an essential measure of functional hearing ability. Results show that ASR-based scoring aligns closely with audiologist evaluations, reinforcing the potential of these models to support clinical decisionmaking and improve access to reliable hearing assessments. A key outcome of this research is the development of an automated, two-version desktop graphical user interface (GUI) for administering SiN tests. This tool facilitates test playback, response recording, and real-time SNR loss computation while enabling seamless de-identified data uploads to cloud-based ASR services, such as Amazon Web Services (AWS) and Microsoft Azure. The study also explores the electroacoustic evaluation of hearing aids under various speech and noise configurations, including the impact of face masks, directional microphone settings, and reverberation levels. Findings reveal that ASR models can effectively process hearing aid test recordings without requiring clean reference signals, offering a more scalable alternative to traditional electroacoustic assessments. To further bridge accessibility gaps, a cross-platform mobile application was developed, integrating an on-device ASR model for self-administered SiN testing. The app enables individuals to assess their speech-in-noise performance remotely, supporting offline functionality for users in areas with limited internet access. Pilot testing with normal-hearing adults demonstrated that the mobile app reliably captures and processes SiN responses across different microphone configurations and loudspeaker setups, achieving performance comparable to cloud-based ASR solutions. This work contributes to the field of audiology by advancing ASR-driven hearing assessments, improving accessibility, and reducing reliance on clinic-based evaluations. By integrating ASR technologies into automated testing frameworks and mobile applications, this research lays the groundwork for more inclusive, efficient, and scalable solutions in hearing healthcare. These findings have direct implications for early intervention strategies, hearing aid optimization, and the broader adoption of tele-audiology solutions in real-world environments.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».