Factor Automaton Audio Transcript Alignment
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Solution Overview
Problem
Existing methods for aligning audio data with transcripts, such as the Viterbi algorithm, face challenges with long audio files and high error rates, leading to inefficient decoding and inaccurate alignments.
Innovation Solution
The use of a factor automaton to represent language models trained on transcripts, allowing for efficient alignment of audio data with transcripts by encoding language elements and accounting for errors and discrepancies through self-loops and recursive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Viterbi algorithm is used to align speech to a transcript, then alignment can be achieved with properly trained acoustic models and a dictionary, but long audio files become difficult to decode and processing efficiency deteriorates
Solution Approach 1:
The patent segments the audio file into multiple smaller audio segments before processing. Each segment is aligned independently using the Viterbi algorithm, which reduces the computational burden and improves decoding efficiency for long audio files while maintaining alignment accuracy through proper segmentation strategies
Solution Approach 2:
The patent performs preliminary actions by pre-processing the audio data, pre-training acoustic models on segment-specific data, and preparing segmentation schemes before the actual alignment process. This preliminary preparation optimizes the subsequent alignment process for both accuracy and efficiency
2Productivity
If traditional decoding algorithms are used on transcripts with high error rates, then alignment processing can be performed, but the high error rates mislead the decoding algorithms and alignment accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the alignment results are evaluated and used to iteratively improve the transcript and acoustic models. Error correction feedback loops allow the system to identify and correct transcription errors, improving alignment accuracy even when initial transcripts have high error rates
Solution Approach 2:
The patent employs dynamic adjustments to the decoding process based on detected error rates. When high error rates are detected, the system dynamically modifies decoding parameters, re-trains acoustic models on corrected data, and adjusts alignment strategies adaptively to maintain accuracy
3Measurement precision
If more computational resources are allocated to improve alignment accuracy for long audio files, then alignment precision can be improved, but computational cost and resource consumption increase
Solution Approach 1:
By segmenting long audio files into smaller parts, the patent reduces the computational resources required for each alignment operation while maintaining overall accuracy. This allows efficient processing of long audio files without requiring excessive computational power for any single operation
Solution Approach 2:
The patent dynamically adjusts processing parameters such as acoustic model complexity, decoding depth, and alignment granularity based on the specific characteristics of each audio segment. This adaptive parameter adjustment optimizes the balance between alignment accuracy and computational resource consumption
Data Source
AI summary
The subject matter of this specification can be implemented in, among other things, a computer-implemented method including receiving audio data and a transcript of the audio data. The method further includes generating a language model including a factor automaton that includes automaton states and arcs, each of the automaton arcs corresponding to a language element from the transcript. The method further includes receiving language elements recognized from the received audio data and times at which each of the recognized language elements occur in the audio data. The method further includes comparing the recognized language elements to one or more of the language elements from the factor automaton to identify times at which the one or more of the language elements from the transcript occur in the audio data. The method further includes aligning a portion of the transcript with a portion of the audio data using the identified times.