Prosody and its application to forensic linguistics

Michael J Harris,Viola G Miglio,Stefan Th Gries

doi:10.5195/lesli.2014.12

Abstract

This article describes three studies in prosody and their potential application to the field of forensic linguistics. It begins with a brief introduction to prosody. It then proceeds to describe Miglio, Gries, & Harris (2014), a comparison of prosodic coding of new information by bilingual Spanish-English speakers and monolingual Spanish speakers. A description of Harris & Gries (2011) follows. This study compares the vowel duration variability of bilingual Spanish-English speakers and monolingual Spanish speakers, and touches upon corpus-based frequency effects and differences in linguistic aptitude between the two speaker groups. Finally, a portion of anongoing study is described (Harris in preparation). This section describes the use of prosodic variables and ensemble methods (or methods that use multiple learning algorithms) to classify languages, even in the case of impoverished data. All three experiments have implications and applications to the field of forensic linguistics, which are touched upon in each respective section and discussed in a more in-depth manner in the final section of this article. Furthermore, the applications of these methods to forensic linguistics are discussed in light of best practices for forensic linguistics, as outlined in Chaski (2013).

Highlights

Speaker recognition largely based upon basic acoustic features of a speaker's voice has achieved some considerable success in the past; for instance, machine-learning algorithms have been successfully trained to match recordings of a speaker with other recordings of the same speaker (e.g. Schötz 2002)
Vowel duration variability has been measured with metrics in an attempt to distinguish between rhythm classes (e.g. Low & Grabe 1995)
While the theoretical implications of this data set still require an inspection of trees in the random forest ensemble in order to determine the directional behavior of these main effects and potential interactions, it does raise an interesting question on the application of highly automated methods to linguistic data set; implementing forensic linguistic research through highly-automated computerized software increases consistency and objectivity; this ties with Chaski’s concept of replicability as a best practice for forensic linguistics (2013)

Summary

Introduction

Speaker recognition largely based upon basic acoustic features of a speaker's voice has achieved some considerable success in the past; for instance, machine-learning algorithms have been successfully trained to match recordings of a speaker with other recordings of the same speaker (e.g. Schötz 2002). Prosody is a facet of phonology that is concerned with rhythm, stress, and intonation This is important because these properties differ across languages (and, at times, dialects), and the nature of the research described is applicable to best practices for forensic linguistics (discussed in the conclusion to this paper). Prosody is often described as concerning itself with the so-called suprasegmental features of the speech signal; this is to say that rather than be confined to a specific segment such as a syllable, it can span across several segments. This distinction does not always prove easy to define, ; rhythm is often measured by the duration of vowels, consonants, or syllables, for instance. We will define prosody as including the aforementioned rhythm, stress, and intonation, and any measurements or metrics used to quantify these properties

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Discussion

Conclusion