{"title":"最大限度地提高音素识别精度,提高语音可理解性噪声","authors":"Petko N. Petkov, G. Henter, W. Kleijn","doi":"10.1109/TASL.2013.2244089","DOIUrl":null,"url":null,"abstract":"An effective measure of speech intelligibility is the probability of correct recognition of the transmitted message. We propose a speech pre-enhancement method based on matching the recognized text to the text of the original message. The selected criterion is accurately approximated by the probability of the correct transcription given an estimate of the noisy speech features. In the presence of environment noise, and with a decrease in the signal-to-noise ratio, speech intelligibility declines. We implement a speech pre-enhancement system that optimizes the proposed criterion for the parameters of two distinct speech modification strategies under an energy-preservation constraint. The proposed method requires prior knowledge in the form of a transcription of the transmitted message and acoustic speech models from an automatic speech recognition system. Performance results from an open-set subjective intelligibility test indicate a significant improvement over natural speech and a reference system that optimizes a perceptual-distortion-based objective intelligibility measure. The computational complexity of the approach permits use in on-line applications.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2244089","citationCount":"33","resultStr":"{\"title\":\"Maximizing Phoneme Recognition Accuracy for Enhanced Speech Intelligibility in Noise\",\"authors\":\"Petko N. Petkov, G. Henter, W. Kleijn\",\"doi\":\"10.1109/TASL.2013.2244089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An effective measure of speech intelligibility is the probability of correct recognition of the transmitted message. We propose a speech pre-enhancement method based on matching the recognized text to the text of the original message. The selected criterion is accurately approximated by the probability of the correct transcription given an estimate of the noisy speech features. In the presence of environment noise, and with a decrease in the signal-to-noise ratio, speech intelligibility declines. We implement a speech pre-enhancement system that optimizes the proposed criterion for the parameters of two distinct speech modification strategies under an energy-preservation constraint. The proposed method requires prior knowledge in the form of a transcription of the transmitted message and acoustic speech models from an automatic speech recognition system. Performance results from an open-set subjective intelligibility test indicate a significant improvement over natural speech and a reference system that optimizes a perceptual-distortion-based objective intelligibility measure. The computational complexity of the approach permits use in on-line applications.\",\"PeriodicalId\":55014,\"journal\":{\"name\":\"IEEE Transactions on Audio Speech and Language Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/TASL.2013.2244089\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Audio Speech and Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TASL.2013.2244089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2244089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maximizing Phoneme Recognition Accuracy for Enhanced Speech Intelligibility in Noise
An effective measure of speech intelligibility is the probability of correct recognition of the transmitted message. We propose a speech pre-enhancement method based on matching the recognized text to the text of the original message. The selected criterion is accurately approximated by the probability of the correct transcription given an estimate of the noisy speech features. In the presence of environment noise, and with a decrease in the signal-to-noise ratio, speech intelligibility declines. We implement a speech pre-enhancement system that optimizes the proposed criterion for the parameters of two distinct speech modification strategies under an energy-preservation constraint. The proposed method requires prior knowledge in the form of a transcription of the transmitted message and acoustic speech models from an automatic speech recognition system. Performance results from an open-set subjective intelligibility test indicate a significant improvement over natural speech and a reference system that optimizes a perceptual-distortion-based objective intelligibility measure. The computational complexity of the approach permits use in on-line applications.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.