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Interference: Path Lengths01:10

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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Effective Zero-Shot Multi-Speaker Text-to-Speech Technique Using Information Perturbation and a Speaker Encoder.

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  • 1Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.

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|December 9, 2023
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Summary

This study introduces an improved Grad-TTS model for zero-shot multi-speaker speech synthesis. The novel approach enables high-quality speech generation for unseen speakers by leveraging speaker information from references.

Keywords:
diffusion modelinformation perturbationzero-shot multi-speaker speech synthesis

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Deep learning has significantly advanced speech synthesis quality.
  • Grad-TTS, a diffusion model, offers high-quality multi-speaker synthesis but struggles with unseen speakers.

Purpose of the Study:

  • To develop an effective zero-shot multi-speaker speech synthesis model.
  • To enable speech synthesis for speakers not present in the training data.

Main Methods:

  • An enhanced Grad-TTS architecture was proposed.
  • A pre-trained speaker recognition model extracts speaker information from references.
  • Information perturbation allows learning diverse speaker characteristics.

Main Results:

  • The model demonstrated excellent performance for seen speakers.
  • Comparable performance was achieved for unseen speakers compared to existing zero-shot models.
  • Objective metrics like speaker encoder cosine similarity (SECS) and mean opinion score (MOS) were used for evaluation.

Conclusions:

  • The proposed method effectively extends Grad-TTS for zero-shot multi-speaker speech synthesis.
  • The approach successfully synthesizes speech for unseen speakers, overcoming previous limitations.