Voice Similarity Analysis

The Problem

Azure Text-to-Speech offers over 500 neural voices across 100+ locales. Products that ship with a specific voice need fallback alternatives for when that voice is deprecated or unavailable. Traditionally, teams selected fallbacks by ear — a subjective process that scales poorly, produces inconsistent results, and lacks an auditable rationale.

Our Solution

This pipeline replaces subjective listening with objective measurement. Each voice synthesizes a standardized script, and a speaker verification model (Resemblyzer) converts each recording into a 256-dimensional embedding — essentially a numerical fingerprint of vocal identity. Cosine similarity between embeddings produces a score from 0 to 1 for every voice pair, enabling reproducible, data-driven fallback rankings.

Methodology

1. Generate a neutral evaluation script and translate it into 23 target languages via Google Translate
2. Synthesize each plain Neural voice reading its locale-specific script using Azure TTS
3. Extract 256-dim speaker embeddings from each recording using Resemblyzer's GE2E model
4. Compute pairwise cosine similarity matrices and rank fallback candidates with same-gender filtering

Results

The production run processed 300 voices across 23 locales with zero synthesis failures. Each voice now has a ranked list of its most similar-sounding same-gender alternatives, with similarity scores color-coded by confidence tier. The interactive dashboard lets stakeholders explore heatmaps and fallback recommendations without running any code.