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Abstract

Technological advances have propelled the research methodology in transcription. Language corpus tools based on statistical models and deep learning have improved the alignment and annotation phases. However, when it comes to transcribing the material, the conversation’s interpretive load and nature themselves hinder automation of the process. That is why interviews used for studying spoken language are still transcribed with a player and keyboard, which can constitute one of the most time-consuming aspects of data processing. In other professional contexts, automatic speech recognition is used to transcribe effectively through human-computer collaboration. The techniques and strategies may differ, but they all stabilize fluctuations in computing tools and are faster than other methods. In this study, the off-line respeaking method was used to transcribe the interviews of the Spoken Corpus of the Spanish Language in Montreal. Transcription times and accuracy were measured and compared with automatic speech recognition and typing. Off-line respeaking, using automatic speech-to-text software in its current state, proved to be the fastest and most error-free method for transcribing interviews.