Solutions for robust recognition over the GSM cellular network

Karray, Lamia; Jelloun, Aibdellatif Ben; Mokbel, Chafic

doi:10.1109/icassp.1998.674417

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…This is similar to a kind of HMM multi-condition training. An alternative set of approaches is to train separate environment-specific HMMs and combine the models during ASR decoding (Karray et al 1998). This approach can also incorporate dedicated models of specific nonstationary noise types, like impulsive noise or frame erasures, which can be trained from labeled examples of occurrences of these noise events in training data.…”

Section: Enhancement Of Asr Performance Ver Mobile Network Omentioning

confidence: 99%

“…This approach can also incorporate dedicated models of specific nonstationary noise types, like impulsive noise or frame erasures, which can be trained from labeled examples of occurrences of these noise events in training data. Karray et al (1998) proposed several examples of this class of approach, each differing in the manner in which the environment specific HMM models were integrated during search. Finally, the most widely discussed class of approaches for robust ASR in the cellular domain is the application of feature compensation techniques to ASR features.…”

Section: Enhancement Of Asr Performance Ver Mobile Network Omentioning

confidence: 99%

See 1 more Smart Citation

Automatic Speech Recognition on Mobile Devices and over Communication Networks

Tan

Lindberg

2008

Advances in Pattern Recognition

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Section: Enhancement Of Asr Performance Ver Mobile Network Omentioning

confidence: 99%

Section: Enhancement Of Asr Performance Ver Mobile Network Omentioning

confidence: 99%

Automatic Speech Recognition on Mobile Devices and over Communication Networks

Tan

Lindberg

2008

Advances in Pattern Recognition

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“…Thus, this paper proposes an in-depth look at the influence of GSM speech coding on text independent speaker recognition performance. To our knowledge, few contributions [1], [2], [3] were made on this subject, whereas the effect of perturbations in a mobile framework has been more extensively studied for automatic speech recognition, where we can cite among others [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

GSM speech coding and speaker recognition

Besacier

Grassi

Dufaux

et al.

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)

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This paper investigates the influence of GSM speech coding on text independent speaker recognition performance. The three existing GSM speech coder standards were considered. The whole TIMIT database was passed through these coders, obtaining three transcoded databases. In a first experiment, it was found that the use of GSM coding degrades significantly the identification and verification performance (performance in correspondence with the perceptual speech quality of each coder). In a second experiment, the features for the speaker recognition system were calculated directly from the information available in the encoded bit stream. It was found that a low LPC order in GSM coding is responsible for most performance degradations. By extracting the features directly from the encoded bit-stream, we also managed to obtain a speaker recognition system equivalent in performance to the original one which decodes and reanalyzes speech before performing recognition.0-7803-6293-4/00/$10.00 02000 IEEE.

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“…In [16], [19], [10], [15], [9], severe ASR performance degradation was observed for a distributed wireless speech recognition system, especially in the case of transmission errors that occur in bursts. Because of rapid fluctuations of received signal strength the mobile radio environment can be a very difficult channel for data transmission.…”

Section: Introductionmentioning

confidence: 99%

An error-protected speech recognition system for wireless communications

Weerackody¹,

Reichl²,

Potamianos³

2002

IEEE Trans. Wireless Commun.

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uture wireless multimedia terminals will have a variety of applications that require speech recognition capabilities.In this paper, we consider a robust distributed speech recognition system where representative parameters of the speech signal are extracted at the wireless terminal and transmitted to a centralized automatic speech recognition (ASR) server. We propose several unequal error protection schemes for the ASR bit stream and demonstrate the satisfactory performance of these schemes for typical wireless cellular channels. In addition, a "soft-feature" error concealment strategy is introduced at the ASR server that uses "soft-outputs" from the channel decoder to compute the marginal distribution of only the reliable features during likelihood computation at the speech recognizer. This soft-feature error concealment techniques reduces the ASR error rate by up to four times for certain channels. Also considered is a channel decoding technique with source information that improves ASR performance. uture wireless multimedia terminals will have a variety of applications that require speech recognition capabilities. In this paper, we consider a robust distributed speech recognition system where representative parameters of the speech signal are extracted at the wireless terminal and transmitted to a centralized automatic speech recognition (ASR) server. We propose several unequal error protection schemes for the ASR bit stream and demonstrate the satisfactory performance of these schemes for typical wireless cellular channels. In addition, a "soft-feature" error concealment strategy is introduced at the ASR server that uses "soft-outputs" from the channel decoder to compute the marginal distribution of only the reliable features during likelihood computation at the speech recognizer. This soft-feature error concealment techniques reduces the ASR error rate by up to four times for certain channels. Also considered is a channel decoding technique with source information that improves ASR performance. F

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Solutions for robust recognition over the GSM cellular network

Cited by 17 publications

References 10 publications

Automatic Speech Recognition on Mobile Devices and over Communication Networks

Automatic Speech Recognition on Mobile Devices and over Communication Networks

GSM speech coding and speaker recognition

An error-protected speech recognition system for wireless communications

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