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Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Masked-speech Recognition Using Human and Synthetic Cloned Speech.

Lauren Calandruccio¹, Mohsen Hariri², Emily Buss³

¹Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA.

Trends in Hearing

|December 8, 2025

Summary

This summary is machine-generated.

Voice cloning technology generates synthetic speech that sounds human-like. This study found voice clones are as intelligible and human-like as real voices, aiding speech recognition research.

Keywords:

AI artificial intelligence speech recognition synthetic speech voice clone

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Last Updated: Jan 9, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Published on: January 5, 2024

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

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

Published on: September 27, 2024

Area of Science:

Speech processing and human-computer interaction.

Background:

Voice cloning synthesizes speech mimicking human vocal characteristics.
Evaluating synthetic speech requires comparison with human speech for intelligibility and human-likeness.

Purpose of the Study:

To compare intelligibility, human-likeness, and perceptual similarity of human and voice-cloned speech.
To assess the utility of voice clones in speech recognition research.

Main Methods:

Masked-sentence recognition tests were conducted with young adults with normal hearing.
Speech from five human talkers and their voice clones was presented in noise at -6 dB SNR.
Participants rated human-likeness and identified voice origins (human vs. clone).

Main Results:

Sentence recognition scores were similar for human and voice-cloned speech, though worse for nonsense than meaningful sentences.
Human-likeness ratings were comparable between human and cloned voices.
Participants accurately identified different voices but struggled to distinguish human/clone pairs, often mistaking clones for humans.

Conclusions:

Voice clones show potential as a reliable tool for evaluating speech recognition, comparable to human speech.
Voice synthesis and automatic speech recognition are valuable for studying human speech perception.