Continuous speech recognition with sparse coding

Smit, Willie J.; Barnard, Etienne

doi:10.1016/j.csl.2008.06.002

Cited by 17 publications

(15 citation statements)

References 31 publications

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“…TI-digits is a large speech database of more than 25 thousand connected digits sequences spoken by over 300 men, women, and children. This speech database is widely used in the literature to assess ASR algorithms on small-vocabulary tasks (Barker et al, 2005;Cooke et al, 2001;Smit & Barnard, 2009). The data were collected in a quiet environment and digitized at 20 kHz.…”

Section: Automatic Speech Recognition Resultsmentioning

confidence: 99%

Acoustic Simulations of Cochlear Implants in Human and Machine Hearing Research

Do¹

2012

Cochlear Implant Research Updates

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IntroductionCochlear implant is an instrument which can be implanted in the inner ear and can restore partial hearing to profoundly deaf people (Loizou, 1999a) (see Fig. 1). Acoustic simulations of cochlear implants are widely used in cochlear implant research. Basically, they are acoustic signals which simulate what the profoundly deaf people could hear when they wear cochlear implants. Useful conclusions can be deduced from the results of experiments performed with acoustic simulations of cochlear implants. There are two typical applications in cochlear implant research which use acoustic simulations of cochlear implants. In the first one, acoustic simulations of cochlear implants are used to define how many independent channels are needed in order to achieve high levels of speech understanding (Loizou et al., 1999). The second application of acoustic simulations in cochlear implants research is for determining the effect of electrode insertion depth on speech understanding (Baskent & Shannon, 2003;Dorman et al., 1997b). In this chapter, we review briefly these conventional applications of acoustic simulations in cochlear implants research and, on the other hand, introduce novel applications of acoustic simulations of cochlear implants, both in cochlear implants research and in other domains, such as automatic speech recognition (ASR) research. To this end, we present quantitative analyses on the fundamental frequency (F0) of the cochlear implant-like spectrally reduced speech (SRS) which are, essentially, acoustic simulations of cochlear implants (Loizou, 1999a). These analyses support the report of , which was based on subjective tests, about the difficulty of cochlear implant users in identifying speakers. Following the results of our analyses, the F0 distortion in state-of-the-art cochlear implants is large when the SRS, which is acoustic simulation of cochlear implant, is synthesized only from subband temporal envelopes (Do, Pastor & Goalic, 2010a). The analyses revealed also a significant reduction of F0 distortion when the frequency modulation is integrated in cochlear implant, as proposed by . Consequently, the results of such quantitative analyses, performed on relevant acoustic traits, could be exploited to conduct subjective studies in cochlear implant research. On the other hand, we investigate the automatic recognition of the cochlear implant-like SRS. Actually, state-of-the-art ASR systems rely on relevant spectral information, extracted from original speech signals, to recognize input speech in a statistical pattern recognition framework. We show that from certain SRS spectral resolution, it is possible to achieve (automatic) recognition performance as good as that attained with the original clean speech even though the cochlear implant-like SRS is synthesized only from subband temporal envelopes of the original clean speech

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Section: Automatic Speech Recognition Resultsmentioning

confidence: 99%

Acoustic Simulations of Cochlear Implants in Human and Machine Hearing Research

Do¹

2012

Cochlear Implant Research Updates

View full text Add to dashboard Cite

show abstract

“…TI-digits is a large speech database of more than 25 thousand connected digits sequences spoken by over 300 men, women, and children. This speech database is widely used in the literature to assess ASR algorithms on smallvocabulary tasks [17,18,19]. The data were collected in a quiet environment and digitized at 20 kHz.…”

Section: Automatic Speech Recognition Resultsmentioning

confidence: 99%

On the Recognition of Cochlear Implant-Like Spectrally Reduced Speech With MFCC and HMM-Based ASR

Pastor

Goalic

2010

IEEE Trans. Audio Speech Lang. Process.

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In this paper, we investigate the recognition of cochlear implantlike spectrally reduced speech (SRS) using conventional speech features (MFCCs and PLP coefficients) and HMM-based ASR. The SRS was synthesized from subband temporal envelopes extracted from original clean speech for testing, whereas the acoustic models were trained on a different set of original clean speech signals of the same speech database. It was shown that changing the bandwidth of the subband temporal envelopes had no significant effect on the ASR word accuracy. In addition, increasing the number of frequency subbands of the SRS from 4 to 16 improved significantly the system performance. Furthermore, the ASR word accuracy attained with the original clean speech, by using both MFCC-based and PLP-based speech features, can be achieved by using the 16-, 24-, or 32-subband SRS. The experiments were carried out by using the TI-digits speech database and the HTK speech recognition toolkit.

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“…Through years of intensive research, sparse coding has been applied successfully in image processing [32]. Recently, sparse coding has been widely used for speech enhancement [33], speech separation [34] and robust speech recognition [35][36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…Smit et al seem to be the first ones who introduced sparse coding into speech recognition [35]. In their method, a waveform is transformed into a spectrogram and sparse representations for the spectrogram are found by means of a linear generative model.…”

Section: Introductionmentioning

confidence: 99%