SNR-dependent waveform processing for improving the robustness of ASR front-end

Macho, Dušan; Cheng, Yan Ming

doi:10.1109/icassp.2001.940828

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Feature vectors are extracted from the output of this waveform processing block. A detailed description of the waveform processing block can be found in [15]. As defined by the ETSI advanced front-end [4], each packet transmitted over the communication channel carries two feature vectors.…”

Section: Resultsmentioning

confidence: 99%

Distributed speech recognition in the presence of noise and packet loss

Flynn¹,

Jones²

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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This paper investigates the benefits of combining speech enhancement and packet loss mitigation in a Distributed Speech Recognition (DSR) system operating over a packet-based network. The recognition task is connected digits with feature vectors extracted from speech using two different auditory-based front-ends. Feature vectors are vector quantized and combined in pairs for transmission over a channel that is subject to packet loss. Results demonstrate that the combination of speech enhancement and packet loss mitigation techniques can greatly improve the speech recognition performance of a DSR system.The use of voice as a user interface technology for data entry to network services is increasing. For mobile wireless devices, small keypads and displays can be an inconvenient method of data entry and the use of reliable voice input can offer a greatly improved input method. With the increasing use of the Internet for the transmission of voice over IP (VoIP), new voice-enabled data services and applications are being developed by service providers. To facilitate access to services by means of voice input there is a need to include automatic speech recognition (ASR) as part of the user interface.In Distributed Speech Recognition (DSR), the speech recognition task is split between the terminal, where the front-end feature extraction is performed, and the network, where the back-end recognition is performed. The features that represent the speech are sent by means of an error protected data channel to the classifier for processing. DSR avoids both the speech encoding and decoding stages associated with centralized (server-based) ASR systems and so eliminates the degradations that originate from the speech compression algorithms that are used in order to achieve low bit-rate transmission. The European Telecommunications Standards Institute (ETSI) published their first standard for DSR in 2000 [1] under the Aurora working group.Recent comparative studies have shown the superior performance of DSR to codec-based ASR [2]. However, in a DSR system transmission errors in the form of random packet loss and packet burst loss still need to be taken into consideration. Such transmission errors can have a significant impact on recognition accuracy. Furthermore, it is well known that the presence of noise severely degrades the performance of speech recognition systems. A large amount of research has been carried out on speech enhancement, and a number of approaches have been well documented in the literature, for example [3]. ETSI has published a second DSR standard [4] which includes enhancement of the speech signal prior to feature extraction.In this paper, the speech recognition performance of the ETSI advanced front-end for DSR [4] is compared with the auditory front-end proposed by Li et al. [5], in the presence of the combined degradations of background noise and packet loss. Speech enhancement and error mitigation are used to compensate for noise and packet loss. The recognition problem examined is connected digit recogn...

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Section: Resultsmentioning

confidence: 99%

Distributed speech recognition in the presence of noise and packet loss

Flynn¹,

Jones²

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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show abstract

“…The speech enhancement algorithms were used on both the Table 1 and the corresponding results for the ETSI basic front-end (the baseline for this test) are given in Table 2. block is to improve the noise robustness in the front-end of an ASR system by enhancing the high SNR period portion and attenuating the low SNR period portion in the waveform time domain, thus increasing the overall SNR of noisy speech [26]. However, the evaluation here is looking primarily at the effect of speech enhancement or noise reduction alone on the connected digit recognition accuracy.…”

Section: Tests and Resultsmentioning

confidence: 99%

“…Feature vectors are extracted from the output of this waveform processing block. A detailed description of the waveform processing block can be found in [26]. The ETSI advanced front-end carries out post-processing in the cepstral domain in the form of blind equalization as described by [13].…”

Section: Tests and Resultsmentioning

confidence: 99%

Robust Distributed Speech Recognition Using Auditory Modelling

Flynn¹,

Jones²

2012

Modern Speech Recognition Approaches With Case Studies

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“…Therefore, the waveform processing block in the ETSI advanced front-end was disabled. A detailed description of the waveform processing block can be found in (Macho and Cheng, 2001).…”

Section: Recognition Testsmentioning

confidence: 99%