On the robust incorporation of formant features into hidden Markov models for automatic speech recognition

Abstract-Recent work in text to speech synthesis has pointed to the benefit of using a continuous pitch estimate; that is, one that records pitch even when voicing is not present. Such an approach typically requires interpolation. The purpose of this paper is to show that a continuous pitch estimation is available from a combination of otherwise well known techniques. Further, in the case of an autocorrelation based estimate, the continuous requirement negates the need for other heuristics to correct for common errors. An algorithm is suggested, illustrated, and demonstrated using a parametric vocoder.

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“…It is also appropriate for incorporation into HMM training in the same way as formants via the algorithm presented by Garner and Holmes [15].…”

Section: Discussionmentioning

confidence: 99%

“…It was shown by Garner and Holmes [15] that uncertainty about the presence of formants can be represented as a variance on their distributions. This can in turn be incorporated into HMM-based models.…”

Section: B Continuous Pitchmentioning

confidence: 99%

A Simple Continuous Pitch Estimation Algorithm

Garner

Cerňak

Motlíček

2013

IEEE Signal Process. Lett.

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“…In de Wet et al (2004), the authors propose a 2-dimension HMM to extract the formant positions and evaluate their potential on a vowel classification task. In Garner and Holmes (1998), the authors integrate the formant estimations into the HMM formalism, in such a way that multiple formant estimate alternatives weighted by a confidence measure are handled. In Tolba et al (2003), a multi-stream approach is used to combine MFCC features with formant estimates and a selection of acoustic cues such as acute/grave, open/close, tense/lax, etc.…”

Section: Auxiliary Acoustic Featuresmentioning

confidence: 99%

Automatic speech recognition and speech variability: A review

BenZeghiba

Mori

Deroo

et al. 2007

Speech Communication

431

207

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Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge.Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, one might expect a representation in terms of formant parameters to be robust against additive noise. Automatically extracted formant-like features have shown some potential for noise robustness in automatic speech recognition, especially when combined with state-of-the-art features (Garner and Holmes, 1998;Weber et al, 2001a;de Wet et al, 2000).Despite its apparent advantages, the formant representation of speech signals has never completely eliminated competing representations. Especially in speech technology there seems to be a strong preference for non-parametric representations of speech signals.…”

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confidence: 99%

Evaluation of formant-like features on an automatic vowel classification task

Wet

Weber

Boves

et al. 2004

The Journal of the Acoustical Society of America

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This study investigates possibilities to find a low-dimensional, formant-related physical representation of speech signals, which is suitable for automatic speech recognition. This aim is motivated by the fact that formants are known to be discriminant features for speech recognition. Combinations of automatically extracted formant-like features and state-of-the-art, noise-robust features have previously been shown to be more robust in adverse conditions than state-of-the-art features alone. However, it is not clear 1 de Wet, JASA how these automatically extracted formant-like features behave in comparison with true formants. The purpose of this paper is to investigate two methods to automatically extract formant-like features, i.e. robust formants and HMM2 features, and to compare these features to hand-labeled formants as well as to mel-frequency cepstral coefficients in terms of their performance on a vowel classification task. The speech data and hand-labeled formants that were used in this study are a subset of the American English vowels database presented in [Hillenbrand et al., J. Acoust. Soc. Am. 97, 3099-3111 (1995)]. Classification performance was measured on the original, clean data as well as in (simulated) adverse conditions. In combination with standard automatic speech recognition methods, the classification performance of the robust formant and HMM2 features compare very well to the performance of the hand-labeled formants.PACS numbers: 43.72.Ne, 43.72.Ar 2 de Wet, JASA I IntroductionHuman speech signals can be described in many different ways (Flanagan, 1972;Rabiner and Schafer, 1978). Some descriptions are directly related to speech production, while others are more suitable for investigating speech perception. Some descriptive frameworks, of which the formant representation is a well-known example, have successfully been applied to both production and perception.Speech production is often modeled as an acoustic source feeding into a linear filter (representing the vocal tract) with little or no interaction between the source and the filter. In terms of this model of acoustic speech production, the phonetically relevant properties of speech signals can be characterized by the resonance frequencies of the filter (to be completed with information on the source, in terms of periodicity and power). It is well known that the frequencies of the first two or three formants are sufficient information for the perceptual identification of vowels (Flanagan, 1972;Minifie et al., 1973). The formant representation is attractive because of its parsimonious character: it allows the representation of speech signals with a very small number of parameters. Not surprisingly, many attempts have been made to exploit the parametric formant representation in speech technology applications such as speech synthesis, speech coding and automatic speech recognition (ASR).A special reason why formants make for an attractive representation of the acoustic characteristics of speech signals is their relation -by virt...

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On the robust incorporation of formant features into hidden Markov models for automatic speech recognition

Cited by 28 publications

References 3 publications

A Simple Continuous Pitch Estimation Algorithm

A Simple Continuous Pitch Estimation Algorithm

Automatic speech recognition and speech variability: A review

Evaluation of formant-like features on an automatic vowel classification task

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