Abstract: Systems, methods, and computer-readable storage media for speech recognition, and more specifically to training domain or accent-specific speech recognition language models. The system can receive a starting ASR (Automated Speech Recognition) neural network model configured to receive audio in the language and generate a starting transcript of the audio. The system can also receive a request to generate a specialized version of the starting ASR neural network model. Based on the request, the system can execute an analysis of the audio, resulting in timestamps and a confidence of each word in the audio. The system can then execute the starting ASR neural network model, resulting in a starting transcript. The system can then generate training data using the timestamps, confidences, and the starting transcript, and train an updated ASR based on the training data, resulting in the specialized version of the starting ASR neural network model.

Abstract: Systems, methods, and computer-readable storage media for selection of domain or accent specific speech recognition language models. The system can receive a list of available ASR (Automated Speech Recognition) models, each of which is associated with a category of speech (e.g., a specific industry or accent), and a request for a transcription of an audio file with audio in one of those categories. The system can select a specific ASR model based on a similarity of the specific category of speech of the audio file and the category of speech of the specific ASR model. Then the system can transmit, to an ASR architecture: the specific ASR model; the audio file; and instructions to generate a transcription of the audio file using the specific ASR model within the ASR architecture. The system can then receive, from the ASR architecture, the transcription of the audio file.

Abstract: Systems, methods, and computer-readable storage media for speech recognition, and more specifically to training domain or accent-specific speech recognition language models. The system can receive a starting ASR (Automated Speech Recognition) neural network model configured to receive audio in the language and generate a starting transcript of the audio. The system can also receive a request to generate a specialized version of the starting ASR neural network model. Based on the request, the system can execute an analysis of the audio, resulting in timestamps and a confidence of each word in the audio. The system can then execute the starting ASR neural network model, resulting in a starting transcript. The system can then generate training data using the timestamps, confidences, and the starting transcript, and train an updated ASR based on the training data, resulting in the specialized version of the starting ASR neural network model.

Abstract: Systems, methods, and computer-readable storage media for making assignments to different speech-to-text engines based on previous transcription scores. An exemplary system can train a model by receiving a first digital audio recording, randomly assigning speech-to-text engines to transcribe the first digital audio recording, and scoring the resulting transcriptions and scoring the engines based on their performances. The system can then generate a model for selecting a speech-to-text engine from within the speech-to-text engines. When a second digital audio recording is received, the system can assign, by executing the model, at least one selected speech-to-text engine from the speech-to-text engines to transcribe the second digital audio recording.

Abstract: The system is used by both Producer and Consumer of digital evidence, which use the system to provide a secure and irrefutable record of a transaction involving the use of the digital evidence to produce new protected digital evidentiary content, e.g. transcription, according to a set of rules and limitations on the use of the digital evidence over a specific period of time which expires after a certain time. The newly create evidentiary content along with security and metadata are evaluated, and results used to confirm that the evidence has been maintained according to the terms and conditions.

Abstract: A method of converting speech to text comprises receiving an audio recording from an input device comprising speech of a plurality of speakers. Extracting from the audio recording, a speaker audio recording comprising recorded audio of an individual speaker. Selecting, based on a characteristic of the speaker audio recording, a speech to text engine and a dictionary. Configuring the speech to text engine with the dictionary and executing a first conversion process to convert a first portion of the speaker audio recording to produce a first transcript. Evaluating a performance metric of the conversion process against a quality metric to reconfigure the speech to text engine and execute a second conversion process to convert a second portion of the speaker audio recording to produce a second transcript. Combining the first transcript and the second transcript to produce a transcript of the speaker audio recording.

Abstract: A system for collecting and evaluating data records comprises a configuration device associated a data collection device associated, and a data monitoring device. The devices are registered with a server. The data collection device transmits a task to the server over a network. The data collection device receives the task from the server and records a data record in response to the task. The data collecting device groups the data record with metadata to produce a verified data record and transmits the verified data record to the server. The data monitoring device receives the verified data record from the server and facilitates the evaluation of the verified data record. The evaluation of the verified data record is used to generate an updated verified data record comprising evaluation results and updated metadata and is transmitted to the server and added to a portfolio.

Abstract: A system for collecting and evaluating data records comprises a configuration device associated a data collection device associated, and a data monitoring device. The devices are registered with a server. The data collection device transmits a task to the server over a network. The data collection device receives the task from the server and records a data record in response to the task. The data collecting device groups the data record with metadata to produce a verified data record and transmits the verified data record to the server. The data monitoring device receives the verified data record from the server and facilitates the evaluation of the verified data record. The evaluation of the verified data record is used to generate an updated verified data record comprising evaluation results and updated metadata and is transmitted to the server and added to a portfolio.