Speech Coding Using Latent Embedding Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech coding technologies face challenges in achieving high-quality audio encoding at low bit rates and efficiently transmitting audio data while maintaining speaker independence and prosodic information.
Innovation Solution
The system employs an encoder neural network to generate a discrete latent representation of audio data using content and speaker latent embedding vectors, which is then decoded by a decoder neural network, allowing for efficient transmission and reconstruction of high-quality speech at low bit rates, with the option to include a pitch reconstruction neural network for prosodic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech coding is used to maintain high audio quality, then speech fidelity is improved, but bit rate increases
Solution Approach 1:
The speech signal is segmented into multiple latent variables representing different acoustic features (spectral envelope, pitch, timing). Each latent variable is independently encoded using separate embedding vectors (content embeddings for spectral features, speaker embeddings for speaker-specific characteristics). This segmentation allows efficient compression while preserving essential speech qualities at low bit rates.
2Adaptability or versatility
If speaker-independent coding is used to generalize across speakers, then adaptability is improved, but speaker-specific information (prosody) is lost
Solution Approach 1:
Different parts of the encoding system handle different aspects of speech with specialized quality: content latent variables capture speaker-independent spectral features using content embedding vectors, while separate speaker latent variables capture speaker-specific prosodic information using speaker embedding vectors. This local specialization allows the system to maintain speaker independence for generalization while preserving speaker-specific prosody through dedicated embedding vectors.
3Measurement precision
If continuous latent representation is used to maintain precision, then measurement precision is improved, but data transmission requirements increase
Solution Approach 1:
Instead of transmitting continuous latent vectors directly, the system uses discrete embedding vectors from predefined codebooks as copies or approximations of the continuous latent representations. The encoder selects the nearest neighboring embedding vector from the codebook for each latent variable, and only transmits the discrete index identifying the selected embedding. This copying approach maintains sufficient precision for high-quality reconstruction while dramatically reducing data transmission requirements.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input audio data. Only the discrete latent representation needs to be transmitted from an encoder system to a decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data.