The corpus contains speech data of 2 Czech native speakers, male and female. The speech is very precisely articulated up to hyper-articulated, and the speech rate is low. The speech data with a highlighted articulation is suitable for teaching foreigners the Czech language, and it can also be used for people with hearing or speech impairment. The recorded sentences can be used either directly, e.g., as a part of educational material, or as source data for building complex educational systems incorporating speech synthesis technology. All recorded sentences were precisely orthographically annotated and phonetically segmented, i.e., split into phones, using modern neural network-based methods.
The corpus contains Czech speech of laryngectomy patients recorded before a surgery causing their voice to be lost in order to preserve the voice which can be later used for personalized text-to-speech system. Individual utterances were selected from the language by a special algorithm to cover as much phonetic and prosodic features as possible.
The FERNET-C5 is a monolingual BERT language representation model trained from scratch on the Czech Colossal Clean Crawled Corpus (C5) data - a Czech mutation of the English C4 dataset. The training data contained almost 13 billion words (93 GB of text data). The model has the same architecture as the original BERT model, i.e. 12 transformation blocks, 12 attention heads and the hidden size of 768 neurons. In contrast to Google’s BERT models, we used SentencePiece tokenization instead of the Google’s internal WordPiece tokenization.
More details can be found in README.txt. Yet more detailed description is available in https://arxiv.org/abs/2107.10042
The same models are also released at https://huggingface.co/fav-kky/FERNET-C5
This text corpus contains a carefully optimized set of sentences that could be used in the process of preparing a speech corpus for the development of personalized text-to-speech system. It was designed primarily for the voice conservation procedure that must be performed in a relatively short period before a person loses his/her own voice, typically because of the total laryngectomy.
Total laryngectomy is a radical treatment procedure which is often unavoidable to save life of patients who were diagnosed with severe laryngeal cancer. In spite of being very effective with respect to the primary treatment, it significantly handicaps the patients due to the permanent loss of their ability to use voice and produce speech. Luckily, the modern methods of computer text-to-speech (TTS) synthesis offer a possibility for "digital conservation" of patient's original voice for his/her future speech communication -- a procedure called voice banking or voice conservation. Moreover, the banking procedure can be undertaken by any person facing voice degradation or loss in farther future, or who is simply is willing to keep his/her voice-print.
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.