Corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono. and Technology Agency of the Czech Republic, project No. TA01030476.
The corpus contains pronunciation lexicon and n-gram counts (unigrams, bigrams and trigrams) that can be used for constructing the language model for air traffic control communication domain. It could be used together with the Air Traffic Control Communication corpus (http://hdl.handle.net/11858/00-097C-0000-0001-CCA1-0). and Technology Agency of the Czech Republic, project No. TA01030476
The corpus consists of recordings from the Chamber of Deputies of the Parliament of the Czech Republic. It currently consists of 88 hours of speech data, which corresponds roughly to 0.5 million tokens. The annotation process is semi-automatic, as we are able to perform the speech recognition on the data with high accuracy (over 90%) and consequently align the resulting automatic transcripts with the speech. The annotator’s task is then to check the transcripts, correct errors, add proper punctuation and label speech sections with information about the speaker. The resulting corpus is therefore suitable for both acoustic model training for ASR purposes and training of speaker identification and/or verification systems. The archive contains 18 sound files (WAV PCM, 16-bit, 44.1 kHz, mono) and corresponding transcriptions in XML-based standard Transcriber format (http://trans.sourceforge.net)
The date of airing of a particular recording is encoded in the filename in the form SOUND_YYMMDD_*. Note that the recordings are usually aired in the early morning on the day following the actual Parliament session. If the recording is too long to fit in the broadcasting scheme, it is divided into several parts and aired on the consecutive days.
The corpus consists of transcribed recordings from the Czech political discussion broadcast “Otázky Václava Moravce“. It contains 35 hours of speech and corresponding word-by-word transcriptions, including the transcription of some non-speech events. Speakers’ names are also assigned to corresponding segments. The resulting corpus is suitable for both acoustic model training for ASR purposes and training of speaker identification and/or verification systems. The archive contains 16 sound files (WAV PCM, 16-bit, 48 kHz, mono) and transcriptions in XML-based standard Transcriber format (http://trans.sourceforge.net)
The database actually contains two sets of recordings, both recorded in the moving or stationary vehicles (passenger cars or trucks). All data were recorded within the project “Intelligent Electronic Record of the Operation and Vehicle Performance” whose aim is to develop a voice-operated software for registering the vehicle operation data.
The first part (full_noises.zip) consists of relatively long recordings from the vehicle cabin, containing spontaneous speech from the vehicle crew. The recordings are accompanied with detailed transcripts in the Transcriber XML-based format (.trs). Due to the recording settings, the audio contains many different noises, only sparsely interspersed with speech. As such, the set is suitable for robust estimation of the voice activity detector parameters.
The second set (prompts.zip) consists of short prompts that were recorded in the controlled setting – the speakers either answered simple questions or they repeated commands and short phrases. The prompts were recorded by 26 different speakers. Each speaker recorded at least two sessions (with identical set of prompts) – first in stationary vehicle, with low level of noise (those recordings are marked by –A_ in the file name) and second while actually driving the car (marked by –B_ or, since several speakers recorded 3 sessions, by –C_). The recordings from this set are suitable mostly for training of the robust domain-specific speech recognizer and also ASR test purposes.