Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder (1603.00982v4)

Abstract: The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length audio segments. These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with very attractive real world applications such as query-by-example Spoken Term Detection (STD). In this STD application, the proposed approach significantly outperformed the conventional Dynamic Time Warping (DTW) based approaches at significantly lower computation requirements. We propose unsupervised learning of Audio Word2Vec from audio data without human annotation using Sequence-to-sequence Audoencoder (SA). SA consists of two RNNs equipped with Long Short-Term Memory (LSTM) units: the first RNN (encoder) maps the input audio sequence into a vector representation of fixed dimensionality, and the second RNN (decoder) maps the representation back to the input audio sequence. The two RNNs are jointly trained by minimizing the reconstruction error. Denoising Sequence-to-sequence Autoencoder (DSA) is furthered proposed offering more robust learning.

• The paper presents an unsupervised model that derives fixed-length audio representations using an LSTM-based encoder-decoder framework.
• It employs a denoising technique in the Sequence-to-sequence Autoencoder to ensure robustness against input variability in audio features.
• Empirical evaluations on the LibriSpeech dataset demonstrate enhanced spoken term detection accuracy compared to traditional retrieval methods.

Unsupervised Audio Segment Representation via Sequence-to-Sequence Autoencoder: The Audio Word2Vec Approach

The research presented in the paper "Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder" explores the potential of creating vector representations for audio segments analogous to the textual Word2Vec representations. This work is anchored in the domain of speech processing and aims at efficiently clustering and querying audio segments based on their phonetic characteristics without relying on supervised learning methods.

Methodological Framework

The authors employ an unsupervised Sequence-to-sequence Autoencoder framework, consisting of two bifurcated LSTM-equipped RNNs: an encoder and a decoder. The encoder transcribes a variable-length sequence of audio features, such as MFCCs, into a fixed-length vector. The decoder subsequently reconstructs the input sequence from the vector, minimizing the reconstruction error in the process. This architecture allows for the derivation of vector representations that encapsulate the phonetic sequences of the original audio input.

An enhancement over the basic Sequence-to-sequence Autoencoder model is proposed in the form of Denoising Sequence-to-sequence Autoencoder (DSA), which integrates noise resilience via denoising techniques. This extension is designed to bolster the robustness of the encoding against variabilities in input audio.

Empirical Evaluation

For experimental validation, the methodology is applied to a query-by-example Spoken Term Detection (STD) task using the LibriSpeech corpus. The results indicate that Audio Word2Vec-based representations significantly enhance the efficiency and accuracy of retrieval tasks in comparison to traditional DTW-based methods. Notably, the vector representations derived through both the SA and DSA models demonstrate increased precision in capturing phonetic similarity, evidenced by mean average precision improvements over competitive baseline methods.

Analytical Insights

The analysis section underscores that vector representations produced by the autoencoder reflect not only the phonetic structures but also demonstrate resilience to differences in suffixes and prefixes. The cosine similarity assessments across segment pairs with variable phoneme differences delineated consistent alignment with phonetic characteristics, highlighting the efficacy of the learned representations in distinguishing subtle audio differences.

Moreover, the DSA model exhibited superior performance over the basic SA model in reflecting phoneme sequence variations — an attribute attributable to its denoising training protocol. This reinforces the capacity of unsupervised models, particularly when supplemented with noise handling techniques, for generating semantically rich audio representations.

Implications and Future Directions

Although this paper demonstrates the feasibility of encoding audio segments for retrieval tasks, its broader implications lie in the potential applications across speech recognition systems, speaker identification, and emotion classification in speech processing. The unsupervised nature of the approach circumvents the exigencies of labeled data, potentially transforming low-resource environments.

For future exploration, the proposed models could be trained on more extensive datasets with additional language diversities. Further refinements in the dimensionality of the encoding space, as well as experimentation with other neural architectures or hybrid models, may enhance model performance across different applications, opening attempts to generalize this approach to real-world, multilingual audio processing tasks. Additionally, application-specific adaptions could enable rapid audio indexing and streamlined integration into existing speech recognition systems.