Abstract
This paper presents a robust automatic speech recognition (ASR) system as multimedia interface for car navigation. In front-end, we use the minimum-mean square error (MMSE) enhancement to suppress the background in-car noise and then compensate the spectrum components distorted by noise over-reduction by smoothing technologies. In acoustic model training, an immunity learning scheme is adopted, in which pre-recorded car noises are artificially added to clean training utterances to imitate the in-car environment. The immunity scheme makes the system robust to both residual noise and speech enhancement distortion. In the context of Mandarin speech recognition, a special issue is the diversification of Chinese dialects, i.e. the pronunciation difference among accents decreases the recognition performance if the acoustic models are trained with an unmatched accented database. We propose to train the models with multiple accented Mandarin databases to solve this problem. The efficiency of the proposed ASR system is confirmed in evaluations.
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References
Gong, Y.: Speech recognition in noisy environments: a survey. Speech Communication 16, 261–291 (1995)
Boll, S.F.: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech and Signal Processing ASSP-27, 113–120 (1979)
Viikki, O., Bye, D., Laurila, K.: A recursive feature vector normalization approach for robust speech recognition in noise. In: Proc. Of ICASSP, pp. 733–736 (1998)
ETSI Standard, Speech processing, transmission and quality aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms, ETSI ES 202 050 v.1.1.1 (October 2002)
Mak, B., Tam, Y., Li, Q.: Discriminative auditory features for robust speech recognition. In: Proc. Of ICASSP, pp. 381–384 (2002)
Gales, M.J.F., Young, S.J.: Robust continuous speech recognition using parallel model combination. IEEE Trans. on SAP 4(5), 352–359 (1996)
Moreno, P.J., Raj, B., Stern, R.M.: A vector Taylor series approach for environment-independent speech recogntion. In: Proc. Of ICASSP, pp. 733–736 (1995)
Shimodaira, H., Sakai, N., Nakai, M., Sagayama, S.: Jacobian joint adaptation to noise, channel and vocal tract length. In: Proc. Of ICASSP, pp. 197–200 (2002)
Ephraim, Y., Malah, D.: Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Trans. Acoustic, Speech, and Signal Processing ASSP-32, 1109–1121 (1984)
Takebayashi, Y., Tsuboi, H., Kanazawa, H.: A robust speech recognition system using word-spotting with noise immunity learning. In: Proc. Of ICASSP, pp. 905–908 (1991)
Liu, Y., Fung, P.: Acoustic and phonetic confusions in accented speech recognition. In: Proc. of Eurospeech, pp. 3033–3036 (2005)
Huang, C., Chang, E., Zhou, J., Lee, K.: Accent modeling based on pronunciation dictionary adaptation for large vocabulary Mandarin speech recognition. In: Proc. of ICSLP, pp. 818–821 (2000)
Liu, Y., Fung, P.: State-dependent phonetic tied mixtures with pronunciation modeling for spontaneous speech recognition. IEEE Trans. on SAP 12(4), 351–364 (2004)
Zheng, Y., Sproat, R., Gu, L., Shafran, I., et al.: Accent detection and speech recognition for Shanghai-accented Mandarin. In: Proc. of Eurospeech, pp. 217–220 (2005)
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. on ASSP 28(4), 357–366 (1980)
Takeda, K., Fujimura, H., Itou, K., et al.: Construction and evaluation of a large in-car speech corpus. IEICE Trans. Inf. & Syst. E88-D(3), 553–561 (2005)
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Ding, P., He, L., Yan, X., Zhao, R., Hao, J. (2006). Robust Mandarin Speech Recognition for Car Navigation Interface. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_36
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DOI: https://doi.org/10.1007/11922162_36
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