SWITCHBOARD is a large multispeaker corpus of conversational speech and text which should be of interest to researchers in speaker authentication and large vocabulary speech recognition. About 2500 conversations by 500 speakers from around the US were collected automatically over T1 lines at Texas Instruments. Designed for training and testing of a variety of speech processing algorithms, especially in speaker verification, it has over an 1 h of speech from each of 50 speakers, and several minutes each from hundreds of others. A time-aligned word for word transcription accompanies each recording. >

SWITCHBOARD: telephone speech corpus for research and development

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Systems and methods are provided for receiving speech and non-speech communications of natural language questions and/or commands, transcribing the speech and non-speech communications to textual messages, and executing the questions and/or commands. The invention applies context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for one or more users presenting questions or commands across multiple domains. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech and non-speech communications and presenting the expected results for a particular question or command.

Systems and methods for responding to natural language speech utterance

A system and method provides universal access to voice-based documents containing information formatted using MIME and HTML standards using customized extensions for voice information access and navigation. These voice documents are linked using HTML hyper-links that are accessible to subscribers using voice commands, touch-tone inputs and other selection means. These voice documents and components in them are addressable using HTML anchors embedding HTML universal resource locators (URLs) rendering them universally accessible over the Internet. This collection of connected documents forms a voice web. The voice web includes subscriber-specific documents including speech training files for speaker dependent speech recognition, voice print files for authenticating the identity of a user and personal preference and attribute files for customizing other aspects of the system in accordance with a specific subscriber.

System and method for providing and using universally accessible voice and speech data files

We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.

https://faculty.washington.edu/fxia/courses/LING580MT/yamada_2001.pdf

A Syntax-based Statistical Translation Model

A method and system are provided for time aligning speech. Speech data is input representing speech signals from a speaker. An orthographic transcription is input including a plurality of words transcribed from the speech signals. A sentence model is generated indicating a selected order of the words in response to the orthographic transcription. In response to the orthographic transcription, word models are generated associated with respective ones of the words. The orthographic transcription is aligned with the speech data in response to the sentence model, to the word models and to the speech data.

System and method for time aligning speech

A voice log-in system is based on a person's spoken name input only, using speaker-dependent acoustic name recognition models in a performing speaker-independent name recognition. In an enrollment phase, a dual pass endpointing procedure defines both the person's full name (broad endpoints), and the component names separated by pauses (precise endpoints). An HMM (Hidden Markov Model) recognition model generator generates a corresponding HMM name recognition model modified by the insertion of additional skip transitions for the pauses between component names. In a recognition/update phase, a spoken-name speech signal is input to an HMM name recognition engine which performs speaker-independent name recognition--the modified HMM name recognition model permits the name recognition operation to accommodate pauses between component names of variable duration.

Voice log-in using spoken name input

A name recognition system (FIG. 1 )used to provide access to a database based on the voice recognition of a proper name spoken by a person who may not know the correct pronunciation of the name. During an enrollment phase (10), for each name-text entered (11) into a text database (12), text-derived recognition models (22) are created for each of a selected number of pronunciations of a name-text, with each recognition model being constructed from a respective sequence of phonetic features (15) generated by a Boltzmann machine (13). During a name recognition phase (20), the spoken input (24,25) of a name (by a person who may not know the correct pronunciation) is compared (26) with the recognition models (22) looking for a pattern match--selection of a corresponding name-text is made based on a decision rule (28).

Voice recognition of proper names using text-derived recognition models

The feasibility of cross-language transfer of speech technology is of increasing concern as the demand for recognition systems in multiple languages grows. The paper presents a systematic study of the relative effectiveness of different methods for seeding and training HMMs in a new language, using transfer from English to Japanese for small vocabulary speaker independent continuous speech recognition as a test case. Effects of limited training data are also explored. The study found that cross-language adaptation produced better models than alternative approaches with relatively little effort, and that the number of speakers is more critical than the number of utterances for small training data sets. >

An evaluation of cross-language adaptation for rapid HMM development in a new language

This invention consists of three enhancements to HMM-based automatic language identification systems. The three enhancements are: (i) language-discriminant acoustic model training and recognition, (ii) an acoustic model pruning procedure that picks only those phonetic models which are considered useful for language identification, and (iii) a neural network-based language classification method that uses knowledge-based features derived from phone sequences output by the HMM phonetic recognizers.

Barbara J. Wheatley

Papers

System and method for time aligning speech

Voice log-in using spoken name input

Voice recognition of proper names using text-derived recognition models

An evaluation of cross-language adaptation for rapid HMM development in a new language

Automatic language identification method and system