You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investigate binary speaker embedding, which transforms i-vectors to binary vectors (codes) by a hash function. We start from locality sensitive hashing (LSH), a simple binarization approach where binary codes are derived from a set of random hash functions. A potential problem of LSH is that the randomly sampled hash functions might be suboptimal. We therefore propose an improved Hamming distance learning approach, where the hash function is learned by a variable-sized block training that projects each dimension of the original i-vectors to variable-sized binary codes independently. Our experiments show that binary speaker embedding can deliver competitive or even better results on both speaker verification and identification tasks, while the memory usage and the computation cost are significantly reduced.

Cited Abstract(s):
[1]: 

[2]: In supervector UBM GMM paradigm, each acoustic file is represented by the mean parameters of a GMM model. This supervector space is used as a data representation space, which has a high dimensionality. Moreover, this space is not intrinsically discriminant and a complete speech segment is represented by only one vector, withdrawing mainly the possibility to take into account temporal or sequential information. This work proposes a new approach where each acoustic frame is represented in a discriminant binary space. The proposed approach relies on a UBM to structure the acoustic space in regions. Each region is then populated with a set of Gaussian models, denoted as “specificities”, able to emphasize speaker specific information. Each acoustic frame is mapped in the discriminant binary space, turning “on” or “off” all the specificities to create a large binary vector. All the following steps, speaker reference extraction, likelihood estimation or decision take place in this binary space. Even if this work is a first step in this avenue, the experiments based on NIST SRE 2008 framework demonstrate the potential of the proposed approach. Moreover, this approach opens the opportunity to rethink all the classical processes using a discrete, binary view.

[3]: Conventional speaker recognition systems perform poorly under noisy conditions. In this paper, we evaluate binary time-frequency masks for robust speaker recognition. An ideal binary mask is a priori defined as a binary matrix where 1 indicates that the target is stronger than the interference within the corresponding time-frequency unit and 0 indicates otherwise. We perform speaker identification and verification using a missing data recognizer under cochannel and other noise conditions, and show that the ideal binary mask provides large performance gains. By employing a speech segregation system that estimates the ideal binary mask, we achieve significant improvements over alternative approaches. Our study, thus, demonstrates that the use of binary masking represents a promising direction for robust speaker recognition.
Binary embedding has not been fully recognized in the speaker recognition community. The limited research focuses on employing the advantages of binary codes in robustness and fast computing. For example, [3] proposed a time-spectral binary masking approach to improve robustness of speaker recognition in conditions with high interference. Besides, [1] presented a solution for large-scale speaker search and indexing under the i-vector model, where the search and indexing algorithm is based on LSH. The work proposed in [2] is more relevant to our proposal. By their approach, a universal background model (UBM) is employed to divide the acoustic space into subregions, and each subregion is populated with a set of Gaussian components. Each acoustic frame is then converted to a binary vector by evaluating the Gaussian components that the frame belongs to, and the frame-level vectors are finally accumulated to produce the segment-level speaker vector. Better robustness compared with the conventional GMM-UBM approach was reported by the authors.