LibriSpeech Alignments

LibriSpeech Alignments represent the high-precision temporal mapping of the LibriSpeech ASR corpus, providing word and phoneme-level timestamps for approximately 1,000 hours of read English speech. In the 2026 AI landscape, these alignments are essential for training state-of-the-art Automatic Speech Recognition (ASR) systems and fine-tuning Text-to-Speech (TTS) models that require granular prosodic control. The datasets are typically generated using advanced forced-alignment tools such as the Montreal Forced Aligner (MFA) or the Kaldi toolkit. They provide the ground truth for duration modeling in neural vocoders and are foundational for researchers benchmarking speech-to-text latency and accuracy. Technically, the data architecture utilizes TextGrid or JSON formats to synchronize the original audio (sampled at 16kHz) with its corresponding orthographic transcriptions. By providing a bridge between raw audio and linguistic units, LibriSpeech Alignments enable developers to build robust voice-to-command systems, automated subtitling engines, and expressive synthetic voices with sub-millisecond precision.