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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Speaker-Independent Phoneme Alignment Using Transition-Dependent States.

John-Paul Hosom¹

¹Center for Spoken Language Understanding, School of Science & Engineering, Oregon Health & Science University, 20000 NW Walker Road, Beaverton, OR 97006 USA, hosom@cslu.ogi.edu.

Speech Communication

|February 18, 2010

Summary

This summary is machine-generated.

This study improves phoneme alignment accuracy for speech technology. The new system achieves 93.36% agreement on the TIMIT corpus, outperforming previous methods.

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Area of Science:

Speech processing
Computational linguistics
Machine learning

Background:

Accurate phoneme location is crucial for automatic speech recognition (ASR) and text-to-speech (TTS) systems.
Human agreement on phoneme boundaries is high, averaging over 93% within 20 milliseconds.
Existing forced alignment systems provide a baseline for phoneme boundary detection.

Purpose of the Study:

To develop an improved phoneme alignment system.
To enhance the accuracy of phoneme boundary detection in speech data.
To achieve state-of-the-art performance on the TIMIT corpus.

Main Methods:

Developed a modified forced alignment system.
Incorporated energy-based features alongside cepstral features.
Utilized state transition probabilities and probabilities of distinctive phonetic features.

Main Results:

The proposed system achieved 93.36% agreement within 20 msec on the TIMIT corpus.
This represents a 22% relative error reduction compared to the baseline system.
The system also showed a 14% error reduction over a non-Hidden Markov Model (HMM) alignment system.

Conclusions:

The modified phoneme alignment system significantly improves accuracy.
The achieved performance sets a new benchmark on the TIMIT corpus.
The findings have implications for advancing speech processing technologies.