This paper provides a description of the preparation, the speakers, the recordings, and the creation of the orthographic transcriptions of the first large scale speech database for Austrian German. It contains approximately 1900 minutes of (read and spontaneous) speech produced by 38 speakers. The corpus consists of three components. First, the Conversation Speech (CS) component contains free conversations of one hour length between friends, colleagues, couples, or family members. Second, the Commands Component (CC) contains commands and keywords which were either read or elicited by pictures. Third, the Read Speech (RS) component contains phonetically balanced sentences and digits. The speech of all components has been recorded at super-wideband quality in a soundproof recording-studio with head-mounted microphones, large-diaphragm microphones, a laryngograph, and with a video camera. The orthographic transcriptions, which have been created and subsequently corrected manually, contain approximately 290 000 word tokens from 15 000 different word types.
This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA). The corpus is composed of several sequences obtained by convolution of dry acoustic events with more than 9000 impulse responses measured in a real apartment equipped with 40 microphones. The acoustic events include in-domain sentences of different typologies uttered by native speakers in four different languages and non-speech events representing typical domestic noises. To increase the realism of the resulting corpus, background noises were recorded in the real home environment and then added to the generated sequences. The purpose of this work is to describe the simulation procedure and the data sets that were created and used to derive the corpus. The corpus contains signals of different characteristics making it suitable for various multi-microphone signal processing and distant speech recognition tasks.