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Toward the ultimate synthesis/recognition system

¹Nippon Telegraph and Telephone (NTT) Human Interface Laboratories, Tokyo, Japan.

Proceedings of the National Academy of Sciences of the United States of America

|October 24, 1995

Summary

This summary is machine-generated.

This paper outlines key challenges in speech synthesis, speech recognition, and speaker recognition technology. Overcoming these hurdles is crucial for advancing human-computer interaction and creating more natural, adaptable voice systems.

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Area of Science:

Speech Technology
Human-Computer Interaction
Artificial Intelligence

Background:

The early 2000s presented significant challenges in developing advanced speech and speaker recognition systems.
Existing technologies lacked naturalness, adaptability, and robust performance across diverse conditions.

Purpose of the Study:

To predict the state of speech synthesis, recognition, and speaker recognition technology in 2001.
To identify critical research problems hindering the development of ultimate synthesis and recognition systems.

Main Methods:

The study involves a forward-looking analysis of research trends and problem areas in speech processing.
It examines challenges in voice production, prosody, quality control, multilingual capabilities, and emotional expression for synthesis.
It also addresses robust recognition against variations, adaptation, knowledge acquisition, spontaneous speech, and emotion recognition.

Main Results:

Key challenges for speech synthesis include natural voice production, meaning-based prosody control, customizable voice quality, and emotional expression.
Speech and speaker recognition face hurdles in handling speech variations, environmental adaptation, automatic knowledge acquisition, and spontaneous speech.
Recognizing emotion in speech is a shared challenge for both recognition tasks.

Conclusions:

Significant research is needed to achieve natural, intelligible, and adaptable speech and speaker recognition systems.
Addressing issues like prosody, voice control, and spontaneous speech is vital for future advancements.
Developing robust evaluation methods and incorporating articulatory/perceptual constraints are essential for progress.