US 2025/0029601 A1 · DETECTING SYNTHETIC SPEECH

Abstract

In general, the disclosure describes techniques for detecting synthetic speech of a speaker. In an example, a machine learning system may be configured to generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, reference embeddings for the speaker that characterize a first set of acoustic features and a first set of phonetic features associated with the speaker. The machine learning system may further be configured to generate, using the deep learning model, a test embedding for an audio clip that characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip. The machine learning system may further be configured to compute a score based on the test embedding and the reference embeddings. The machine learning system may further be configured to output, based on the score, an indication of whether the audio clip includes synthetic speech.

Claims20

Dependent claims

A method for detecting synthetic speech of a speaker in an audio clip, comprising:

generating, using a deep learning model trained to distinguish between synthetic speech and authentic speech, one or more reference embeddings for the speaker, wherein the one or more reference embeddings characterize a first set of acoustic features and a first set of phonetic features associated with the speaker;
generating, using the deep learning model trained to distinguish between synthetic speech and authentic speech, a test embedding for an audio clip that characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip;
computing a score based on the test embedding and the one or more reference embeddings;
and
outputting, based on the score, an indication of whether the audio clip includes synthetic speech.

2 more elements

A computing system comprising processing circuitry and memory for executing a machine learning system, the machine learning system configured to:

generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, one or more reference embeddings for the registered speaker, wherein the one or more reference embeddings specify characterize a first set of acoustic features and a first set of phonetic features associated with the registered speaker;
generate, using the deep learning model trained to distinguish between synthetic speech and authentic speech, a test embedding for an audio clip that specifies characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip;
compute a score based on the test embedding and the one or more reference embeddings;
and
output, based on the score, an indication of whether the audio clip includes synthetic speech.

2 more elements

Computer-readable storage media comprising machine readable instructions for configuring processing circuitry to:

generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, one or more reference embeddings for the registered speaker, wherein the one or more reference embeddings specify characterize a first set of acoustic features and a first set of phonetic features associated with the registered speaker;
generate, using a deep learning model trained to distinguish between synthetic speech and authentic speech, a test embedding for an audio clip that specifies characterizes a second set of acoustic features and a second set of phonetic features associated with the audio clip;
compute a score based on the test embedding and the one or more reference embeddings;
and
output, based on the score, an indication of whether the audio clip includes synthetic speech.

2 more elements

Dependencies are inferred from the claim text.

Detecting synthetic speech

Life of this patent

Detecting synthetic speech

Life of this patent

Abstract

Claims20