Frontiers in Communication (Jun 2022)
Does Automatic Speech Recognition (ASR) Have a Role in the Transcription of Indistinct Covert Recordings for Forensic Purposes?
Abstract
The transcription of covert recordings used as evidence in court is a huge issue for forensic linguistics. Covert recordings are typically made under conditions in which the device needs to be hidden, and so the resulting speech is generally indistinct, with overlapping voices and background noise, and in many cases the acoustic record cannot be analyzed via conventional phonetic techniques (i.e. phonetic segments are unclear, or there are no cues at all present acoustically). In the case of indistinct audio, the resulting transcripts that are produced, often by police working on the case, are often questionable and despite their unreliable nature can be provided as evidence in court. Injustices can, and have, occurred. Given the growing performance of automatic speech recognition (ASR) technologies, and growing reliance on such technologies in everyday life, a common question asked, especially by lawyers and other legal professionals, is whether ASR can solve the problem of what was said in indistinct forensic audio, and this is the main focus of the current paper. The paper also looks at forced alignment, a way of automatically aligning an existing transcriptions to audio. This is an area that needs to be explored in the context of forensic linguistics because transcripts can technically be “aligned” with any audio, making it seem as if it is “correct” even if it is not. The aim of this research is to demonstrate how automatic transcription systems fare using forensic-like audio, and with more than one system. Forensic-like audio is most appropriate for research, because there is greater certainty with what the speech material consists of (unlike in forensic situations where it cannot be verified). Examples of how various ASR systems cope with indistinct audio are shown, highlighting that when a good-quality recording is used ASR systems cope well, with the resulting transcript being usable and, for the most part, accurate. When a poor-quality, forensic-like recording is used, on the other hand, the resulting transcript is effectively unusable, with numerous errors and very few words recognized (and in some cases, no words recognized). The paper also demonstrates some of the problems that arise when forced-alignment is used with indistinct forensic-like audio—the transcript is simply “forced” onto an audio signal giving completely wrong alignment. This research shows that the way things currently stand, computational methods are not suitable for solving the issue of transcription of indistinct forensic audio for a range of reasons. Such systems cannot transcribe what was said in indistinct covert recordings, nor can they determine who uttered the words and phrases in such recordings, nor prove that a transcript is “right” (or wrong). These systems can indeed be used advantageously in research, and for various other purposes, and the reasons they do not work for forensic transcription stems from the nature of the recording conditions, as well as the nature of the forensic context.
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