Phoneme Similarity Matrices to Improve Long Audio Alignment for Automatic Subtitling

• Main Authors: Ruiz, Pablo, Álvarez, Aitor, Arzelus, Haritz
• Other Authors: Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice), Département Littératures et langage (LILA), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Cité (USPC)-Université Sorbonne Nouvelle - Paris 3, VicomTech
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: automatic subtitling; long audio alignment; phoneme similarity matrices; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01099239; https://hal.archives-ouvertes.fr/hal-01099239/document; https://hal.archives-ouvertes.fr/hal-01099239/file/387_Paper.pdf

International audience Long audio alignment systems for Spanish and English are presented, within an automatic subtitling application. Language-specific phone decoders automatically recognize audio contents at phoneme level. At the same time, language-dependent grapheme-to-phoneme modules perform a transcription of the script for the audio. A dynamic programming algorithm (Hirschberg's algorithm) finds matches between the phonemes automatically recognized by the phone decoder and the phonemes in the script's transcription. Alignment accuracy is evaluated when scoring alignment operations with a baseline binary matrix, and when scoring alignment operations with several continuous-score matrices, based on phoneme similarity as assessed through comparing multivalued phonological features. Alignment accuracy results are reported at phoneme, word and subtitle level. Alignment accuracy when using the continuous scoring matrices based on phonological similarity was clearly higher than when using the baseline binary matrix.