Perception of Alcoholic Intoxication in Speech

The ALC sub-challenge of the Interspeech Speaker State Chal-lenge (ISSC) aims at the automatic classification of speech sig-nals into intoxicated and sober speech. In this context we con-ducted a perception experiment on data derived from the same corpus to analyze the human performance on the same task. The results show that human still outperform comparable baseline results of ISSC. Female and male listeners perform on the same level, but there is strong evidence that intoxication in female voices is easier to be recognized than in male voices. Prosodic features contribute to the decision of human listeners but seem not to be dominant. In analogy to Doddington’s zoo of speaker verification we find some evidence for the existence of lambs and goats but no wolves. Index Terms: alcoholic intoxication, speech perception, forced choice, intonation, Alcohol Language Corpu

BAStat : New Statistical Resources at the Bavarian Archive for Speech Signals

A new type of language resource ’BAStat’ has been released by the Bavarian Archive for Speech Signals. In contrast to primary resources like speech and text corpora BAStat comprises statistical estimates based on a number of primary resources: first and second order occurrence probability of phones, syllables and words, duration statistics, probabilities of pronunciation variants of words and probabilities of context information. Unlike other statistical speech resources BAStat is based solely on recordings of conversational German and therefore models spoken language. It consists of 7-bit ASCII tables and matrices to maximize inter-operability between different platforms and can be downloaded from the BAS web-site. This paper gives a detailed description about the empirical basis, the contained data types, some interesting interpretations and a brief comparison to the text-based statistical resource CELEX

Estimating Speaking Rate by Means of Rhythmicity Parameters

In this paper we present a speech rate estimator based on so-called rhythmicity features derived from a modified version of the short-time energy envelope. To evaluate the new method, it is compared to a traditional speech rate estimator on the basis of semi-automatic segmentation. Speech material from the Alcohol Language Corpus (ALC) covering intoxicated and sober speech of different speech styles provides a statistically sound foundation to test upon. The proposed measure clearly correlates with the semi-automatically determined speech rate and seems to be robust across speech styles and speaker states

MAUS Goes Iterative

In this paper we describe further developments of the MAUS system and announce a free-ware software package that may be downloaded from the ’Bavarian Archive for Speech Signals’ (BAS) web site. The quality of the MAUS output can be considerably improved by using an iterative technique. In this mode MAUS will calculated a first pass through all the target speech material using the standard speaker-independent acoustical models of the target language. Then the segmented and labelled speech data are used to re-estimated the acoustical models and the MAUS procedure is applied again to the speech data using these speaker-dependent models. The last two steps are repeated iteratively until the segmentation converges. The paper describes the general algorithm, the German benchmark for evaluating the method as well as some experiments on German target speakers

Automatic Phonetic Transcription of Non-Prompted Speech

A reliable method for automatic phonetic transcription of non− prompted German speech has been developed at th