Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech (1803.08976v2)

Abstract: In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions.

• The paper introduces Speech2Vec, a novel framework that learns word embeddings directly from speech using an RNN encoder-decoder architecture.
• It employs both skipgram and continuous bag-of-words strategies, capturing semantic nuances from acoustic data and outperforming traditional Word2Vec on 13 benchmarks.
• The study demonstrates efficiency gains by bypassing transcription, paving the way for advanced applications in speech recognition, synthesis, and audio-based understanding.

Speech2Vec: Advancements in Learning Word Embeddings from Speech

The paper presented in the paper "Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech" introduces a novel framework, Speech2Vec, that addresses the challenge of deriving semantically significant vector representations for words directly from audio input. This research is positioned within the field of NLP, where a significant focus has been on transforming words into vectorized forms, known as word embeddings, using context derived from text. However, Speech2Vec diverges by leveraging acoustic properties inherent in speech, thus tapping into data not captured in written language.

Methodology

Speech2Vec is constructed upon a Recurrent Neural Network (RNN) Encoder-Decoder framework, borrowing methodologies from the popular Word2Vec model, particularly skipgrams and continuous bag-of-words (cbow). Unlike Word2Vec, which relies on a simple two-layered neural network for processing text data, Speech2Vec employs RNNs to handle the complex, variable-length sequences of acoustic features. This framework is adept at capturing the nuances of speech, including prosody and intonation, which convey semantic meaning beyond textual equivalences.

The training of the Speech2Vec model uses either the skipgram approach, which predicts neighboring words from a current word representation, or the cbow model, which infers a current word from nearby words. Both approaches aim to position semantically similar words closely within the resultant embedding space.

Experimental Evaluation

The efficacy of the Speech2Vec model is validated through intrinsic evaluation against 13 established benchmarks for word similarity, where it consistently outperforms the text-based Word2Vec model. This advantage is particularly noted with the skipgrams variant of Speech2Vec, which demonstrates superior results across varied word similarity benchmarks, highlighting its capability to integrate semantic information from audio more effectively than text-based methods.

Tables within the paper reveal that Speech2Vec's performance does not strictly adhere to standard expectations regarding embedding size; although increasing the dimension size generally improves performance, word embeddings of 50-dimensions were frequently sufficient. Additionally, extensive experimentation illustrates that skipgrams Speech2Vec is superior to its cbow counterpart, a result attributed to the model's robustness when trained on smaller corpora.

Implications and Future Directions

The introduction of Speech2Vec presents significant implications for the broader adoption of speech-based data in NLP applications. By bypassing the need for transcription, this model reduces error rates and computational overhead, providing a more efficient pipeline for speech processing. The integration of prosodic and acoustic features directly into word embeddings holds promise for a variety of applications, including speech recognition and synthesis, audio-based translation, and more nuanced speech understanding systems.

Future developments could explore the application of Speech2Vec embeddings in extrinsic tasks like machine listening comprehension or visual question answering, where the model's ability to grasp semantic content from audio data could be particularly transformative. Additionally, the research could extend to less supervised environments, leveraging the model's capabilities in situations where text transcription is unavailable or infeasible.

In summary, Speech2Vec represents a significant stride toward exploiting the full semantic potential of spoken language in NLP, moving beyond the limitations of text and opening new avenues for research and application in the domain of AI-driven language technologies.