USING AUTOMATIC SPEECH RECOGNITION AND SPEECH SYNTHESIS TO IMPROVE THE INTELLIGIBILITY OF COCHLEAR IMPLANT USERS IN REVERBERANT LISTENING ENVIRONMENTS
| Autor: | Leslie M. Collins, Kevin M. Chu, Boyla O. Mainsah |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Reverberation
Computer science medicine.medical_treatment Speech recognition 020206 networking & telecommunications Speech synthesis 02 engineering and technology Intelligibility (communication) computer.software_genre Article 03 medical and health sciences 0302 clinical medicine Cochlear implant 0202 electrical engineering electronic engineering information engineering medicine Active listening 030223 otorhinolaryngology computer |
| Zdroj: | Proc IEEE Int Conf Acoust Speech Signal Process ICASSP |
| ISSN: | 1520-6149 |
| Popis: | Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate the recognized text into anechoic speech. However, the strategy was trained and tested on the same reverberant environment, so it is unknown whether the strategy is robust to unseen environments. Thus, the current study investigated the performance of the previously proposed algorithm in multiple unseen environments. First, an ASR system was trained on anechoic and reverberant speech using different room types. Next, a speech synthesizer was trained to generate speech from the text predicted by the ASR system. Experiments were conducted in normal hearing listeners using vocoded speech, and the results showed that the strategy improved speech intelligibility in previously unseen conditions. These results suggest that the ASR-synthesis strategy can potentially benefit CI users in everyday reverberant environments. |
| Databáze: | OpenAIRE |
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