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Media Mining Indexer
If you are looking for a high end speech recognition product that can automate the process of indexing live news feeds and multimedia archives in Real Time, your search ends here.
With the Media Mining Indexer you can process speech from multiple sources in various formats and produce annotated text output in Real Time. The XML output is uploaded as input into diverse digital asset management systems for subsequent multimedia search and retrieval.
The advanced speech technologies can inherently handle variations in speaking styles as well as recording environments for different domains.
Distributed architecture and built-in Application Programming Interfaces facilitate integration with your existing infrastructure.
Time tagging of each word helps to rapidly identify and access the target segments that interest you. Media Monitoring is made easier for you by identification of key speakers and categorization of raw audio into specific topics. Monitoring news simultaneously in multiple languages like English, German, French, Spanish and Arabic.
The Media Mining Indexer serves as an extremely cost effective solution for your media monitoring needs.
Best-of-breed speech recognition technology enables accurate conversion of spontaneous speech to text, even with high levels of background noise
Speaker Change Detection segregates audio segments based on speaker for focused information retrieval
Speaker Identification is your answer to "who" has been saying "what" in the news
Topic Detection helps locate stories based on your favorite topics with ease
Named Entity Detection makes finding of relevant information simpler as words belonging to categories like persons, locations, organizations etc are highlighted within the text output
Keyword Translation gives immediate access to information contained in foreign language news content
Scalable Architecture supports single machine to multi machine configuration, providing a cost effective solution for small and medium sized TV stations and media monitoring companies
Industry standard XML output facilitates integration with complementary technologies
Media Mining Indexer is designed to perform in real time or faster with common off-the-shelf PC hardware. We support hardware running Windows XP or Windows Server 2003 on Intel or AMD CPUs; typically a Pentium-4 3.4 GHz Hyper-Threading CPU with 2GB of memory.
The minimum hardware configuration to run Media Mining Indexer is 2GB of memory and a single Pentium-III/733 processor (will run approximately 2 times slower than real-time).
The Media Mining Indexer requires a sound card if you feed input other than PCM audio files.
To feed video from Cable-TV, Video recorders or other input devices using Composite input or S-Video input, Media Mining Indexer provides you with the ability to use a WDM compatible analog TV card as input. SAIL LABS uses the following TV cards:
Configuration hints for real-time indexing:
DirectX 9.0 or later. This is required for indexing mpeg, mp3, wmv, avi files using the optional Media Feeder program.
ActivePerl-5.8.7.813 from ActiveState. This can be downloaded from http://ftp.activestate.com/ActivePerl/Windows/5.8/ActivePerl-5.8.7.813-MSWin32-x86-148120.msi. Note: Only the mentioned version of ActivePerl is guaranteed to work with Media Mining Indexer.
Real Player (current version); this optional component should be installed if you need to index Real Media files; this can be downloaded from www.real.com
The open architecture of the Media Mining Indexer enables easy integration with complementary technologies for diverse applications. Some of the products which have integrated the Media Mining Indexer are Virage Videologger®, Oracle 9i interMedia, blue order's media archive® etc. The integration can be done at two levels:
Two command line tools are currently available: a Transcriber tool, and an Indexer tool. Both provide different and complementary functionalities, and they must be used together in order to obtain the complete Indexer process.
The API facilitates communication with the Media Mining System components. It provides a set of C++ classes that represent proxies for the interface components. These classes are in an API Library Header file. There are two typical usage scenarios for the Media Mining Indexer API: for an Audio Feeder Client and an Administrator Client.
An Audio Feeder Client would typically perform the following actions:
An Administrator Client would typically do the following:
The Media Mining System harnesses the synergies of some of the best speech processing and language technologies produced or currently in development. Technologies such as Automatic Speech Recognition, Speaker Identification, Name Spotting, Topic Classification, and Story Segmentation have been integrated in our system, and together produce comprehensively indexed text files from the media stream input.
Automatic Speech recognition is performed in a sequence of steps; it first processes the incoming audio, then chunks the audio into sections of speech and non-speech, and then applies speech-recognition to those segments identified as containing speech. This can be done in real-time, for large vocabularies (64K entries) and for 8 and 16kHz (but is not limited to these).
Our speech recognition engine is language independent (modulo changes on acoustic front end, e.g. for tonal languages). Languages the recognizer has been run in include: English, French, Spanish, German, Arabic and Mandarin.
Front-End: Standard MFCC coefficients, energy+ 5/3 frame regression, deltas, delta-deltas, cepstral mean subtraction.
Acoustic Models: Speaker- and Gender-independent acoustic models, 3- and 5-phone context models, with Gaussian Prototypes tied at the prototype as well as mixture weight levels.
Language Models: Our engine uses mainly Bigrams and Trigrams for the Language Model. We adopt a Witten-Bell type back-off model. The Language Model and Acoustic scores are combined for maximum accuracy.
Decoder Search: We use a multi-pass, time-synchronous search. During processing, increasingly detailed models are used at each step. After a forward pass and a backward pass, the resulting N-best list is re-scored using the most detailed acoustic models.
The Speaker Identification (SID) system identifies speakers in the area of broadcast news transcription. The incoming audio is first split up according to speech / non-speech regions and speaker turns are hypothesized on these chunks. Speaker clustering (SC) and Speaker ID (SID) are run on the resulting chunks. Typically from about 20 to 100 speakers can be identified; for non-target speakers the gender is detected and the unknown speaker's segments labeled accordingly. Speaker ID/Clustering is language independent.
Speaker Identification: SID uses Gaussian Mixture Models (GMM) for a number of pre-selected target speakers and an additional number of cohort speakers who serve for normalization and gender detection purposes.
Speaker Clustering: Speaker Clustering is based on clustering pre-determined chunks (from initial segmentation) into k clusters. As quality measure the within class dispersion is used. Segments are clustered using a variant of the generalized likelihood ratio criterion.
Speaker Change Detection (SCD) is done using a phone-level decoder, which employs a set of broad phonetic classes of speech sounds as well as non-speech sounds. Using the information produced by the decoder (i.e. the "transcript"), the SCD system sequentially hypothesizes speaker turns at phoneme boundaries. A generalized likelihood ratio test is used to determine whether a change should be made.
SCD works on the cepstral features and energy; no derivatives are used. The acoustic models employed are tied on the phoneme level with a language model based on the phoneme classes.
Story Detection consists of several phases. First an episode is partitioned into sections of homogeneous topics. The boundaries of these sections are adjusted further in a subsequent step. Finally, these stable sections are scored against a statistical model representing the individual topics. The top ranking topics are used to determine what the story is about.
Support Vector Machines, one model per topic and one model to model general language (all those filler words which really aren't specific to any topic). Each state represents the topic and emits topic dependent words probabilistically. Transitions between topics are allowed at every word and are not observed. At decoding time, the most likely set of topics given the recognized text is determined.
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