Automatic Phonetic Transcription of Non − Prompted Speech

Automatic Segmentation" (MAUS) system labels and segments the phonetic constituents of spoken German in a manner similar to highly trained phoneticians. MAUS has been used to train automatic speech recognition (ASR) systems as well as to provide detailed statistical analyses of spontaneous speech (using the Verbmobil I and RVG I corpora). The MAUS system is a reliable, automatic means of testing linguistic hypotheses concerning the phonetic properties of spontaneous speech and should therefore play an important role in providing the sort of empirical data required to develop more realistic models of spoken language. 1. INTRODUCTION In many cases our scientific work with recorded non− prompted or even spontaneous German during the last 5 years ended in results that often differ from our text book knowledge of German phonetics. In the light of these observations it is my opinion that the speech sciences including phonetics should follow a new way (beside the traditional ways that are of course still to be pursued!) to comply with the problem that often the scientific models of speech differ significantly from reality. Therefore, in part 2 I will give some arguments for computational methods on the basis of large purpose− independent speech corpora. To give an example of this type of work the third section gives a brief description of the 'Munich Automatic Segmentation' (MAUS) method, while the last part will give three examples where results from MAUS were used in different experiments or applications. The first example is a statistical evaluation of well known assimilation processes at word boundaries; the second and third example describe experiments to improve Automatic Speech Recognition (ASR) by exploiting the knowledge about pronunciation from the MAUS segmentation.