Robust pitch detection in a noisy telephone environment

Doddington, George R.; Secrest, Bruce G.

doi:10.1109/icassp.1987.1169473

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…This class of algorithms uses a sophisticated optimization procedure that evaluates several measures of correlation and spectral change in the signal, and computes an optimal fundamental frequency pattern and voicing pattern simultaneously. Finally, an algorithm that is rarely used in real-time speech systems, but often used for research experimentation, is an algorithm that operates on the cepstrum of the speech signal (Picone et al 1987). This algorithm is still popular today as an accurate method for estimating the fundamental frequency in extremely quiet laboratory recording conditions.…”

Section: Fundamental Frequencymentioning

confidence: 99%

Front end analysis of speech recognition: a review

Anusuya¹,

Katti²

2011

Int J Speech Technol

102

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Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software. But despite of all these advances, machines can not match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. So, today significant portion of speech recognition research is focused on speaker independent speech recognition problem. Before recognition, speech processing has to be carried out to get a feature vectors of the signal. So, front end analysis plays a important role. The reasons are its wide range of applications, and limitations of available techniques of speech recognition. So, in this report we briefly discuss the different aspects of front end analysis of speech recognition including sound characteristics, feature extraction techniques, spectral representations of the speech signal etc. We have also discussed the various advantages and disadvantages of each feature extraction technique, along with the suitability of each method to particular application.

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Section: Fundamental Frequencymentioning

confidence: 99%

Front end analysis of speech recognition: a review

Anusuya¹,

Katti²

2011

Int J Speech Technol

102

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“…A third class of algorithms, similar in nature to the previous class, is based on dynamic programming concepts [44]. These algorithms have been shown to provide high performance across a wide range of environments, including noisy telecommunications channels.…”

Section: A Fundamental Frequencymentioning

confidence: 99%

Signal modeling techniques in speech recognition

1993

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We have seen three important trends develop in the last five years in speech recognition. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or timederivative, spectral information, have become common. Second, similariry transform techniques, often used to normalize and decorrelate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal's spectrum can be estimated in a closed-loop manner. In this paper, we review the signal processing components of these algorithms. These algorithms are presented as part of a unified view of the signal parameterization problem in which there are three major tasks: measurement, transformation, and statistical modeling. This paper is by no means a comprehensive survey of all possible techniques of signal modeling in speech recognition. There are far too many algorithms in use today to make an exhaustive survey feasible (and cohesive). Instead, this paper is meant to serve as a tutorial on signal processing in state-of-the-art speech recognition systems and to review those techniques most commonly used. In keeping with this goal, a complete mathematical description of each algorithm has been included in the paper.

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“…For our experiments, we compared our eigenstructure pitch estimates with the hand-edited pitch estimates and the highly optimized integrated correlator method, both appearing in [9]. The methods abbreviated EDT and ICC are the hand-edited pitch estimates and the integrated correlator method, both from [9]. EIG stands for our eigenstructure method.…”

Section: Preliminary Evaluationsmentioning

confidence: 99%

“…In order to test our methods, we have used three files at different signal-to-noise ratios excised from the Texas Instruments Long Distance Pitch Detection Database described in [9]. This database has a good distribution of files at different SNR's from which we could choose.…”

Section: Preliminary Evaluationsmentioning

confidence: 99%

“…In section four, we combine the results of sections two and three and show that there is some justification for doing so. We give some preliminary evaluations using speech data excised from the Texas Intruments Long Distance Telephone Pitch Detection database of [9] in section five. Finally, we offer some conclusions and future directions in section six.…”

Section: Introductionmentioning

confidence: 99%

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Eigenstructure based pitch estimation in speech processing applications

Andrews¹

[1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems &Amp; Computers

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In this paper we continue to develop eigenstructure based techniques used in the pitch detection and estimation problems in speech processing. Some success with the pitch tracking problem has already been investigated and reported in [l-31. In [l-31, we show through some theoretical and empirical findings that signifrcant enhancement to the cepstral based pitch estimation method [4-51 is possible.

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Robust pitch detection in a noisy telephone environment

Cited by 9 publications

References 4 publications

Front end analysis of speech recognition: a review

Front end analysis of speech recognition: a review

Signal modeling techniques in speech recognition

Eigenstructure based pitch estimation in speech processing applications

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