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While human evaluation is the most reliable metric for evaluating speechgeneration systems, it is generally costly and time-consuming. Previous studieson automatic speech quality assessment address the problem by predicting humanevaluation scores with machine learning models. However, they rely onsupervised learning and thus suffer from high annotation costs and domain-shiftproblems. We propose SpeechLMScore, an unsupervised metric to evaluategenerated speech using a speech-language model. SpeechLMScore computes theaverage log-probability of a speech signal by mapping it into discrete tokensand measures the average probability of generating the sequence of tokens.Therefore, it does not require human annotation and is a highly scalableframework. Evaluation results demonstrate that the proposed metric shows apromising correlation with human evaluation scores on different speechgeneration tasks including voice conversion, text-to-speech, and speechenhancement.

SpeechLMScore: Evaluating speech generation using speech language model