SpeakRight Framework - Helps to build Speech Recognition Applications

We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.

SpeakRight Framework - Helps to build Speech Recognition Applications

SpeakRight is an Java framework for writing speech recognition applications in VoiceXML. Dynamic generation of VoiceXML is done using the popular StringTemplate templating framework. Although VoiceXML uses a similar web architecture as HTML, the needs of a speech app are very different. SpeakRight lives in application code layer, typically in a servlet. The SpeakRight runtime dynamically generates VoiceXML pages, one per HTTP request.

Applications are written in Java using SpeakRight's extensible classes.

http://speakrightframework.blogspot.com/

Tags	text-to-speech speech-recognition speech voicexml java-framework framework
Implementation	Java
License	EPL
Platform	OS-Independent

Related Projects

stt-benchmark - speech to text benchmark framework

Python

This is a minimalist and extensible framework for benchmarking different speech-to-text engines. It has been developed and tested on Ubuntu 18.04 with Python3.6. This framework has been developed by Picovoice as part of the project Cheetah. Cheetah is Picovoice's speech-to-text engine specifically designed for IoT applications. Deep learning has been the main driver in recent improvements in speech recognition. But due to stringent compute/storage limitations of IoT platforms it is most beneficial to the cloud-based engines. Picovoice's proprietary deep learning technology enables transferring these improvements to IoT platforms with much lower CPU/memory footprint. The goal is to be able to run Cheetah on any platform with a C Compiler and a few MB of memory.

speech-recognition speech-to-text deepspeech

tensorflow-speech-recognition - 🎙Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks

Python

Speech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks. Replaces caffe-speech-recognition, see there for some background.

tensorflow speech-recognition neural-network deep-learning stt speech-to-text

Festival - Speech Synthesis System

Festival offers a general framework for building speech synthesis systems as well as including examples of various modules. It offers full text to speech through a APIs via shell and though a Scheme command interpreter. It has native support for Apple OS. It supports English and Spanish languages.

text-to-speech speech-recognition speech

p5.speech - Web Audio Speech Synthesis / Recognition for p5.js

Javascript

p5.speech is a JavaScript library that provides simple, clear access to the Web Speech and Speech Recognition APIs, allowing for the easy creation of sketches that can talk and listen. It consists of two object classes (p5.Speech and p5.SpeechRec) along with accessor functions to speak and listen for text, change parameters (synthesis voices, recognition models, etc.), and retrieve callbacks from the system. Speech recognition requires launching from a server (e.g. a python simpleserver on a local machine).

audio speech-synthesis speech-recognition text-to-speech

kalliope - Kalliope is a framework that will help you to create your own personal assistant.

Python

Kalliope is a framework that will help you to create your own personal assistant. The concept is to create the brain of your assistant by attaching an input signal (vocal order, scheduled event, MQTT message, GPIO event, etc..) to one or multiple actions called neurons.

raspberry bot-creation jarvis personal-assistant speech-to-text speech-recognition speech-synthesis bot home-automation

CMU Sphinx - Toolkit For Speech Recognition

CMUSphinx toolkit is a speech recognition toolkit with various tools used to build speech applications. CMU Sphinx toolkit has a number of packages for different tasks. Pocketsphinx — lightweight recognizer library written in C, Sphinxbase — support library required by Pocketsphinx, Sphinx4 — adjustable, modifiable recognizer written in Java, CMUclmtk — language model tools, Sphinxtrain — acoustic model training tools, Sphinx3 — decoder for speech recognition research written in C.

speech speech-recognition speech-to-text ivr

lingvo - Lingvo

Python

Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models. A list of publications using Lingvo can be found here.

nlp research translation tensorflow machine-translation speech distributed tts speech-synthesis mnist speech-recognition lm seq2seq speech-to-text gpu-computing language-model asr

espnet - End-to-End Speech Processing Toolkit

Shell

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition, and end-to-end text-to-speech. ESPnet uses chainer and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

speech-recognition deep-learning end-to-end chainer pytorch kaldi speech-synthesis

HTK - Speech Recognition Toolkit

The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide.

speech speech-recognition speech-to-text tools

FreeTTS - Speech Synthesizer in Java

Java

FreeTTS is a speech synthesis system written entirely in the Java. It is based upon Flite, a small run-time speech synthesis engine developed at Carnegie Mellon University. Flite is derived from the Festival Speech Synthesis System from the University of Edinburgh and the FestVox project from Carnegie Mellon University. FreeTTS supports a subset of the JSAPI 1.0 java speech synthesis specification.

text-to-speech speech-recognition speech

MARY - Text-to-Speech System

Java

MARY is an open-source, multilingual Text-to-Speech Synthesis platform written in Java. It supports German, British and American English, Telugu, Turkish, and Russian.

text-to-speech speech-recognition speech

TensorFlowASR - :zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2

Jupyter

:zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords

tensorflow speech-recognition jasper automatic-speech-recognition speech-to-text ctc conformer deepspeech2 tflite rnn-transducer end2end tensorflow2 contextnet tflite-model tflite-convertion subword-speech-recognition streaming-transducer

voice-elements - :speaker: Web Component wrapper to the Web Speech API, that allows you to do voice recognition and speech synthesis using Polymer

HTML

Web Component wrapper to the Web Speech API, that allows you to do voice recognition (speech to text) and speech synthesis (text to speech) using Polymer. Or download as ZIP.

sonus - :speech_balloon: /so.nus/ STT (speech to text) for Node with offline hotword detection

Javascript

Sonus lets you quickly and easily add a VUI (Voice User Interface) to any hardware or software project. Just like Alexa, Google Now, and Siri, Sonus is always listening offline for a customizable hotword. Once that hotword is detected your speech is streamed to the cloud recognition service of your choice - then you get the results. Generally, running npm install should suffice. This module however, requires you to install SoX.

speech speech-recognition speech-to-text voice-control stt node hotword-detection keyword-spotting alexa voice-recognition keyword spotting hotword detection voice

kaldi-gstreamer-server - Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork

Python

This is a real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framework and implemented in Python. 2018-04-25: Server should now work with Tornado 5 (thanks to @Gastron). If using Python 2, you might need to install the futures package (pip install futures).

speech-recognition

Speech Server .NET

CSharp

Speech Server .NET aims to add functionalities of Text-To-Speech (TTS) and Automatic Speech Recnognition (ASR) to handheld devices like Pocket PC and Smartphone, running Windows Mobile, that are wirelessly connected to a server. This server is able to generate a speech stream ...

speech asr compact-framework library mobile nectar

Voice Conference Manager

Java

Voice Conference Manager uses VoiceXML and CCXML to control speech recognition, text to speech, and voice biometrics for a telephone conference service. Say the names or numbers of people and VCM places them into the call. Can be hosted on public servers

delta - DELTA is a deep learning based natural language and speech processing platform.

Python

DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.

nlp deep-learning tensorflow speech sequence-to-sequence seq2seq speech-recognition text-classification speaker-verification nlu text-generation emotion-recognition tensorflow-serving tensorflow-lite inference asr serving front-end

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

Python

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

pytorch ner sequence-labeling crf lstm-crf char-rnn char-cnn named-entity-recognition part-of-speech-tagger chunking neural-networks nbest lstm cnn batch

nodejs-speech - Node

Javascript

The Cloud Speech API enables easy integration of Google speech recognition technologies into developer applications. Send audio and receive a text transcription from the Cloud Speech API service. Select or create a Cloud Platform project.