What are common metrics for evaluating TTS quality?

Common Metrics for Evaluating TTS Quality Text-to-speech (TTS) systems are evaluated using a mix of subjective and objective metrics to assess how natural, intelligible, and accurate the synthesized speech sounds. These metrics help developers identify strengths and weaknesses in TTS models, ensuring improvements align with human perception and technical benchmarks. Below, we’ll explore three primary categories of evaluation methods: subjective listening tests, objective signal-based measurements, and automated algorithmic scores.

Subjective Listening Tests Subjective evaluations involve human listeners rating synthesized speech on qualities like naturalness, clarity, and emotional expressiveness. The most common method is the Mean Opinion Score (MOS), where listeners rate speech samples on a scale (e.g., 1–5). For example, a MOS of 4.0 might indicate near-human quality, while 2.5 suggests noticeable artificiality. Another approach is Comparative MOS (CMOS), where listeners compare two TTS outputs directly. While subjective tests are reliable, they require significant time and resources to gather statistically meaningful results. Developers often use platforms like Amazon Mechanical Turk to crowdsource ratings, but inconsistencies in listener backgrounds can introduce variability.

Objective Signal-Based Metrics Objective metrics quantify differences between synthesized and reference (natural) speech signals. Mel-Cepstral Distortion (MCD) measures spectral differences by comparing mel-frequency cepstral coefficients (MFCCs) of synthetic and natural audio—lower MCD values indicate better quality. Word Error Rate (WER) evaluates intelligibility by transcribing TTS output with an automatic speech recognition (ASR) system and comparing it to the original text. For instance, a WER of 5% implies high accuracy, while 20% suggests mispronunciations or artifacts. Duration-based metrics, like phoneme duration error, assess prosody by measuring timing mismatches. While efficient, these metrics don’t fully capture perceptual quality, as minor signal differences might not affect human ratings.

Automated Algorithmic Scores Recent advancements use machine learning models to predict subjective ratings without human listeners. For example, TTS MOS predictors are neural networks trained on MOS datasets to estimate naturalness scores directly from audio. Tools like Google’s Mean Opinion Score Prediction (MOSNet) or NVIDIA’s Tacotron-based evaluators fall into this category. Another approach is Speaker Similarity Scores, which use embeddings (e.g., from pre-trained speaker verification models) to measure how well a TTS system mimics a target speaker’s voice. These automated methods are scalable but require large, diverse training datasets to generalize across languages and accents.

In practice, developers combine multiple metrics. For example, a TTS pipeline might use MCD and WER during training to optimize model parameters, followed by MOS tests before deployment. Balancing efficiency and accuracy ensures both technical and perceptual quality are addressed.