Analyzing the Effects of Transcription Errors on Summary Generation of Bengali Spoken Documents

Automatic speech recognition (ASR) has become an indispensable part of the AI domain, with various speech technologies reliant on it. The quality of speech recognition depends on the amount of annotated data used to train an ASR system, among other factors. For a low-resourced language, this is a severe constraint and thus ASR quality is often poor. Humans can read through text containing ASR-errors, provided the context of the sentence is preserved. Yet in cases of transcripts generated by ASR systems of low-resource languages, multiple important words are misrecognized and the context is mostly lost; discerning such a text becomes nearly impossible. This paper analyzes the types of transcription errors that occur while generating ASR transcripts of spoken documents in Bengali, an under-resourced language predominantly spoken in India and Bangladesh. The transcripts of the Bengali spoken document are generated using the ASR of Google Cloud Speech. The paper also explores if there is an effect of such transcription errors in generating speech summaries of these spoken documents. Summarization is carried out extractively; sentences are selected from the ASR-generated text of the spoken document. Speech summaries are created by aggregating the speech-segments from the original speech of the selected sentences. Subjective evaluation shows the ‘readability’ of the spoken summaries are not degraded by ASR errors, but the quality is affected due to the reliance on intermediate text-summary containing transcription errors.