A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings

Abstract

Synchronisation of a voice recording with the corresponding text is a common task in speech and music processing, and is used in many practical applications (automatic subtitling, audio indexing, etc.). A common approach derives a mid-level feature from the audio and finds its alignment to the text by means of maximizing a similarity measure via Dynamic Time Warping (DTW). Recently, a Connectionist Temporal Classification (CTC) approach was proposed that directly emits character probabilities and uses those to find the optimal text-to-voice alignment. While this method yields promising results, the memory complexity of the optimal alignment search remains quadratic in input lengths, limiting its application to relatively short recordings. In this work, we describe how recent improvements brought to the textbook DTW algorithm can be adapted to the CTC context to achieve linear memory complexity. We then detail our overall solution and demonstrate that it can align text to several hours of audio with a mean alignment error of 50 ms for speech, and 120 ms for singing voice, which corresponds to a median alignment error that is below 50 ms for both voice types. Finally, we evaluate its robustness to transcription errors and different languages.