Related Research Articles
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition.
The following outline is provided as an overview of and topical guide to software engineering:
ITK is a cross-platform, open-source application development framework widely used for the development of image segmentation and image registration programs. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with an MRI scan in order to combine the information contained in both.
CMU Sphinx, also called Sphinx for short, is the general term to describe a group of speech recognition systems developed at Carnegie Mellon University. These include a series of speech recognizers and an acoustic model trainer (SphinxTrain).
Julius is a speech recognition engine, specifically a high-performance, two-pass large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. It can perform almost real-time computing (RTC) decoding on most current personal computers (PCs) in 60k word dictation task using word trigram (3-gram) and context-dependent Hidden Markov model (HMM). Major search methods are fully incorporated.
As of the early 2000s, several speech recognition (SR) software packages exist for Linux. Some of them are free and open-source software and others are proprietary software. Speech recognition usually refers to software that attempts to distinguish thousands of words in a human language. Voice control may refer to software used for communicating operational commands to a computer.
ECTACO Inc. is a US-based developer and manufacturer of hardware and software products for speech recognition and electronic translation. They also make jetBook eBook readers.
A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems.
Speaker diarisation is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker. It can enhance the readability of an automatic speech transcription by structuring the audio stream into speaker turns and, when used together with speaker recognition systems, by providing the speaker’s true identity. It is used to answer the question "who spoke when?" Speaker diarisation is a combination of speaker segmentation and speaker clustering. The first aims at finding speaker change points in an audio stream. The second aims at grouping together speech segments on the basis of speaker characteristics.
Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source Natural Language Processing (NLP) system that extracts clinical information from electronic health record unstructured text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context, and negated/not negated.
Kaldi is an open-source speech recognition toolkit written in C++ for speech recognition and signal processing, freely available under the Apache License v2.0.
John Mylopoulos is a Greek-Canadian computer scientist, Professor at the University of Toronto, Canada, and at the University of Trento, Italy. He is known for his work in the field of conceptual modeling, specifically the development an agent-oriented software development methodology. called TROPOS.
The IARPA Babel program developed speech recognition technology for noisy telephone conversations. The main goal of the program was to improve the performance of keyword search on languages with very little transcribed data, i.e. low-resource languages. Data from 26 languages was collected with certain languages being held-out as "surprise" languages to test the ability of the teams to rapidly build a system for a new language.
openSMILE is source-available software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under a source-available license. For commercial use of the tool, the company audEERING offers custom license options.
Speechmatics is a technology company based in Cambridge, England, which develops automatic speech recognition software (ASR) based on recurrent neural networks and statistical language modelling. Speechmatics was originally named Cantab Research Ltd when founded in 2006 by speech recognition specialist Dr. Tony Robinson.
Voice computing is the discipline that develops hardware or software to process voice inputs.
OpenVINO toolkit is a free toolkit facilitating the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware. The toolkit has two versions: OpenVINO toolkit, which is supported by open source community and Intel Distribution of OpenVINO toolkit, which is supported by Intel. OpenVINO was developed by Intel. The toolkit is cross-platform and free for use under Apache License version 2.0. The toolkit enables a write-once, deploy-anywhere approach to deep learning deployments on Intel platforms, including CPU, integrated GPU, Intel Movidius VPU, and FPGAs.
References
- ↑ Rybach, D.; C. Gollan; G. Heigold; B. Hoffmeister; J. Lööf; R. Schlüter; H. Ney (September 2009). "The RWTH Aachen University Open Source Speech Recognition System". Interspeech-2009: 2111–2114. doi:10.21437/Interspeech.2009-604. S2CID 17986018.