In this paper, a new method for spoken mixed language understanding is presented. By mixed language, we mean that the words included in one sentence may come from different languages, a primary language and a secondar...
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In large-vocabulary, speaker-independent speech recognition systems, modeling of vocabulary words by subword units is mandatory. This paper studies the use of triphone units for Mandarin speech recognition compared to...
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We propose a mixed language query disambiguation approach by using co-occurrence information from monolingual data only. A mixed language query consists of words in a primary language and a secondary language. Our met...
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Using TI digits recognition experiments, we show that a combination of two dynamic speech features, Liftered Forward Masked (LFM) MFCC and 2-D cepstrum, can improve system robustness to additive Volvo noise while main...
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The performance of speech recognition systems degrades when speaker accent is different from that in the training set. Accent-independent or accent-dependent recognition both require collection of more training data. ...
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The performance of speech recognition systems degrades when speaker accent is different from that in the training set. Accent-independent or accent-dependent recognition both require collection of more training data. In this paper, we propose a faster accent classification approach using phoneme-class models. We also present our findings in acoustic features sensitive to a Cantonese accent, and possibly other Asian language accents. In addition, we show how we can rapidly transform a native accent pronunciation dictionary to that for accented speech by simply using knowledge of the native language of the foreign speaker. The use of this accent-adapted dictionary reduces recognition error rate by 13.5%, similar to the results obtained from a longer, data-driven process.
We propose a new confidence score for decoding and verification. Since the traditional log likelihood ratio (LLR) is borrowed from speaker verification technique, it may not be appropriate for decoding because we do n...
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ISBN:
(纸本)0780350413
We propose a new confidence score for decoding and verification. Since the traditional log likelihood ratio (LLR) is borrowed from speaker verification technique, it may not be appropriate for decoding because we do not have a good modelling and definition of LLR for decoding/utterance verification. We have proposed a new formulation of LLR that can be used for decoding and verification task. Experimental results show that our proposed LLR can perform equally well compared with the result based on maximum likelihood in a decoding task. Also, we get an 5% improvement in decoding compared with traditional LLR.
This book constitutes the refereed proceedings of the 13th International Conference on Social Robotics, ICSR 2021, held in Singapore, Singapore, in November 2021. The conference was held as a hybrid event.
ISBN:
(数字)9783030905255
ISBN:
(纸本)9783030905248
This book constitutes the refereed proceedings of the 13th International Conference on Social Robotics, ICSR 2021, held in Singapore, Singapore, in November 2021. The conference was held as a hybrid event.
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