A Comparison of Front-Ends for Bitstream-Based ASR over IP

Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses. In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-Antolín, C. Pelàez-Moreno, F. Díaz-de-María, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-Antolín, F. Díaz-de-María, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.Publicad

Efficient Spectral Power Estimation on an Arbitrary Frequency Scale

The Fast Fourier Transform is a very efficient algorithm for the Fourier spectrum estimation, but has the limitation of a linear frequency scale spectrum, which may not be suitable for every system. For example, audio and speech analysis needs a logarithmic frequency scale due to the characteristic of a human’s ear. The Fast Fourier Transform algorithms are not able to efficiently give the desired results and modified techniques have to be used in this case. In the following text a simple technique using the Goertzel algorithm allowing the evaluation of the power spectra on an arbitrary frequency scale will be introduced. Due to its simplicity the algorithm suffers from imperfections which will be discussed and partially solved in this paper. The implementation into real systems and the impact of quantization errors appeared to be critical and have to be dealt with in special cases. The simple method dealing with the quantization error will also be introduced. Finally, the proposed method will be compared to other methods based on its computational demands and its potential speed

On the Computation of the Kullback-Leibler Measure for Spectral Distances

Efficient algorithms for the exact and approximate computation of the symmetrical Kullback-Leibler (1998) measure for spectral distances are presented for linear predictive coding (LPC) spectra. A interpretation of this measure is given in terms of the poles of the spectra. The performances of the algorithms in terms of accuracy and computational complexity are assessed for the application of computing concatenation costs in unit-selection-based speech synthesis. With the same complexity and storage requirements, the exact method is superior in terms of accuracy