On the use of acoustic features for automatic disambiguation of homophones in spontaneous German. (November 2018) - Schuppler, Barbara; Schrank, Tobias
Highlights: Hypothesis: in spontaneous speech, homophones differ in their phonetic detail. 193 acoustic features and random forests are used for homophone disambiguation. Temporal features result to be most relevant to automatic homophone disambiguation. Model with acoustic features outperforms lexical model by 26% F 1 . Abstract: Homophones pose serious issues for automatic speech recognition (ASR) as they have the same pronunciation but different meanings or spellings. Homophone disambiguation is usually done within a stochastic language model or by an analysis of the homophonous word's context, similarly to word sense disambiguation. Whereas this method reaches good results in read speech, it fails in conversational, spontaneous speech, where utterances are often short, contain disfluencies and/or are realized syntactically incomplete. Phonetic studies, however, have shown that words that are homophonous in read speech often differ in their phonetic detail in spontaneous speech. Whereas humans use phonetic detail to disambiguate homophones, this linguistic information is usually not explicitly incorporated into ASR systems. In this paper, we show that phonetic detail can be used to automatically disambiguate homophones using the example of German pronouns. Using 3179 homophonous tokens from a corpus of spontaneous German and a set of acoustic features, we trained a random forest model. Our results show that homophones can be disambiguated reasonably well using acoustic features (74% F 1, 92% accuracy). In particular, this model is able to outperform a model based on lexical context (48% F 1, 89% accuracy). This paper is of relevance for speech technologists and linguists: amodule using phonetic detail similar to the presented model is suitable to be integrated in ASR systems in order to improve recognition. An approach similar to the work here that combines the automatic extraction of acoustic features with statistical analysis is suitable to be integrated in phonetic analysis aiming at finding out more about the contribution and interplay of acoustic features for functional categories.