HMM-based channel error mitigation and its application to distributed speech recognition

Peinado, Antonio M.; Sánchez, Victoria; Pérez-Córdoba, José L.; Torre, Ángel de la

doi:10.1016/s0167-6393(03)00048-7

Cited by 32 publications

(24 citation statements)

References 13 publications

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“…Both the DSR and the transcoder-based approach proposed could be further improved if this type of information combined with MMSE estimation techniques were used [21], [22]. On the other hand, since the techniques proposed along this paper do not require reliability measures, they could be straightforwardly applied to IP networks (lossy packets channels), by considering bad frames as lost frames.…”

Section: Discussionmentioning

confidence: 99%

Recognition of coded speech transmitted over wireless channels

Gómez

Peinado

Sánchez

et al. 2006

IEEE Trans. Wireless Commun.

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Abstract-Network-based speech recognition (NSR) and distributed speech recognition (DSR) have been proposed as solutions to translate speech recognition technologies to mobile environments. NSR is the most straightforward solution since it does not require any modification in the mobile phone, however DSR offers higher robustness against codec compression and transmission channel degradation. This paper explores an alternative approach for remote speech recognition which combines the advantages of NSR and DSR. In this scheme, a standard speech codec is used for speech transmission but the recognition is performed from the received codec parameters. In particular, we focus on the effect of transmission channel errors, which can cause a more severe performance reduction on speech recognition than codec distortion. First, we show that an NSR solution can approach DSR through a reconstruction technique along with an adapted noise reduction technique originally proposed for acoustic noise. Then, these results are improved by working with recognition features directly extracted from the codec bitstream by means of parameter transcoding. Required modifications on current networks in order to access the bitstream are described. The network upgrading with the tandem free operation (TFO) protocol is an attractive solution. This upgrade not only offers an overall improvement on the end-to-end speech quality, but would also allow a recognition performance similar, and even higher in poor channel conditions, to that obtained by DSR when parameter transcoding along with the proposed mitigation techniques are applied.

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Section: Discussionmentioning

confidence: 99%

Recognition of coded speech transmitted over wireless channels

Gómez

Peinado

Sánchez

et al. 2006

IEEE Trans. Wireless Commun.

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“…In previous papers [23], [24], we showed that minimum mean square error (MMSE) estimation is a powerful technique to mitigate the errors introduced by a wireless channel. In [23], [24] it is assumed that all vectors are received at the back end, although affected by channel distortion.…”

Section: Mmse Estimation With Vq Replicasmentioning

confidence: 99%

“…In [23], [24] it is assumed that all vectors are received at the back end, although affected by channel distortion. In wireless channels, transmission errors can be observed at the receiver as bit errors.…”

Section: Mmse Estimation With Vq Replicasmentioning

confidence: 99%

“…Thus, we can now estimate the feature vector at time as (5) where , the conditional probabilities, can be computed as (6) where and are the forward and backward conditional probabilities, respectively, defined as (7) (8) These conditional probabilities can be computed from the transition probabilities and observation probabilities of the HMM model through the following forward and backward recursions (9) (10) where is a normalization factor applied at each step in the recursion (further details can be found in [23]). The following initial conditions are applied to and :…”

Section: A Mmse Formulation For Bursty Distortionsmentioning

confidence: 99%

“…In our previous work [23] we named this technique as forward-backward MMSE (FBMMSE) estimation in order to remark the use of past and future frames by introducing the forward and backward probabilities and a decoding delay. Further details of this technique, along with an analysis of the complexity of the HMM-based MMSE estimation, can be found in [24].…”

Section: A Mmse Formulation For Bursty Distortionsmentioning

confidence: 99%

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Combining Media-Specific FEC and Error Concealment for Robust Distributed Speech Recognition Over Loss-Prone Packet Channels

Gómez

Peinado

Sánchez

et al. 2006

IEEE Trans. Multimedia

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Abstract-This paper presents a mixed recovery scheme for robust distributed speech recognition (DSR) implemented over a packet channel which suffers packet losses. The scheme combines media-specific forward error correction (FEC) and error concealment (EC). Media-specific FEC is applied at the client side, where FEC bits representing strongly quantized versions of the speech vectors are introduced. At the server side, the information provided by those FEC bits is used by the EC algorithm to improve the recognition performance. We investigate the adaptation of two different EC techniques, namely minimum mean square error (MMSE) estimation, which operates at the decoding stage, and weighted Viterbi recognition (WVR), where EC is applied at the recognition stage, in order to be used along with FEC. The experimental results show that a significant increase in recognition accuracy can be obtained with very little bandwidth increase, which may be null in practice, and a limited increase in latency, which in any case is not so critical for an application such as DSR.

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Automatic Speech Recognition on Mobile Devices and over Communication Networks

Tan

Lindberg

2008

Advances in Pattern Recognition

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HMM-based channel error mitigation and its application to distributed speech recognition

Cited by 32 publications

References 13 publications

Recognition of coded speech transmitted over wireless channels

Recognition of coded speech transmitted over wireless channels

Combining Media-Specific FEC and Error Concealment for Robust Distributed Speech Recognition Over Loss-Prone Packet Channels

Automatic Speech Recognition on Mobile Devices and over Communication Networks

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