An integrated pitch tracking algorithm for speech systems

Secrest, Bruce G.; Doddington, George R.

doi:10.1109/icassp.1983.1172016

Cited by 67 publications

(17 citation statements)

References 4 publications

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“…The • Cepstrum pitch determination (CPD), Noll, 1967 • Feature-based pitch tracker (FBPT), Phillips, 1985 • Harmonic product spectrum (HPS), Schroeder, 1968 • Integrated pitch tracking algorithm (IPTA), Secrest & Doddington, 1983 • Parallel processing method (PP), Gold & Rabiner, 1969 • Super resolution pitch determinator (SRPD), Medan et al, 1991…”

Section: Performance Comparisonmentioning

confidence: 99%

Maximum likelihood pitch estimation using sinusoidal modeling

Mahadevan

Espy‐Wilson

2011

2011 International Conference on Communications and Signal Processing

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The aim of the work presented in this thesis is to automatically extract the fundamental frequency of a periodic signal from noisy observations, a task commonly referred to as pitch estimation. An algorithm for optimal pitch estimation using a maximum likelihood formulation is presented. The speech waveform is modeled using sinusoidal basis functions that are harmonically tied together to explicitly

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Section: Performance Comparisonmentioning

confidence: 99%

Maximum likelihood pitch estimation using sinusoidal modeling

Mahadevan

Espy‐Wilson

2011

2011 International Conference on Communications and Signal Processing

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“…The advantage of using DP in pitch tracking is to incorporate continuity constraints across adjacent frames to reduce pitch doubling and halving errors ͑Secrest and Doddington, 1983;Talkin, 1995;Geoffrois, 1996;Droppo and Acero, 1998͒. This is typically achieved by incorporating a transition cost in the DP score function to penalize large changes in neighboring f 0 hypotheses.…”

Section: Dp Searchmentioning

confidence: 99%

A versatile pitch tracking algorithm: From human speech to killer whale vocalizations

Shapiro

Wang

2009

The Journal of the Acoustical Society of America

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In this article, a pitch tracking algorithm ͓named discrete logarithmic Fourier transformation-pitch detection algorithm ͑DLFT-PDA͔͒, originally designed for human telephone speech, was modified for killer whale vocalizations. The multiple frequency components of some of these vocalizations demand a spectral ͑rather than temporal͒ approach to pitch tracking. The DLFT-PDA algorithm derives reliable estimations of pitch and the temporal change of pitch from the harmonic structure of the vocal signal. Scores from both estimations are combined in a dynamic programming search to find a smooth pitch track. The algorithm is capable of tracking killer whale calls that contain simultaneous low and high frequency components and compares favorably across most signal to noise ratio ranges to the peak-picking and sidewinder algorithms that have been used for tracking killer whale vocalizations previously.

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“…We estimated fundamental frequency (F0) using an algorithm similar to that presented in [9]. F0 values, short-time energy and zero-crossing rate (ZCR) were computed every 10ms.…”

Section: Acoustic Featuresmentioning

confidence: 99%

Automatic classification of question turns in spontaneous speech using lexical and prosodic evidence

Ananthakrishnan

Ghosh

Narayanan

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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The ability to identify speech acts reliably is desirable in any spoken language system that interacts with humans. Minimally, such a system should be capable of distinguishing between questionbearing turns and other types of utterances. However, this is a non-trivial task, since spontaneous speech tends to have incomplete syntactic, and even ungrammatical, structure and is characterized by disfluencies, repairs and other non-linguistic vocalizations that make simple rule based pattern learning difficult. In this paper, we present a system for identifying question-bearing turns in spontaneous multi-party speech (ICSI Meeting Corpus) using lexical and prosodic evidence. On a balanced test set, our system achieves an accuracy of 71.9% for the binary question vs. non-question classification task. Further, we investigate the robustness of our proposed technique to uncertainty in the lexical feature stream (e.g. caused by speech recognition errors). Our experiments indicate that classification accuracy of the proposed method is robust to errors in the text stream, dropping only about 0.8% for every 10% increase in word error rate (WER).

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An integrated pitch tracking algorithm for speech systems

Abstract: A pitch tracking algorithm is described which operates in the time domain from a conditioned linear prediction residual and applies dynamic programming to optimally determine both pitch and voicing.

Cited by 67 publications

References 4 publications

Maximum likelihood pitch estimation using sinusoidal modeling

Maximum likelihood pitch estimation using sinusoidal modeling

A versatile pitch tracking algorithm: From human speech to killer whale vocalizations

Automatic classification of question turns in spontaneous speech using lexical and prosodic evidence

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