###### Abstract  
Recurrent neural networks (RNNs) can model the time-dependency of time-series data. It has also been widely used in text-dependent speaker verification to extract speaker-and-phrase-discriminant embeddings. As with other neural networks, RNNs are trained in mini-batch units. In order to feed input sequences into an RNN in mini-batch units, all the sequences in each mini-batch must have the same length. However, the sequences have variable lengths and we have no knowledge of these lengths in advance. Truncation/padding are most commonly used to make all sequences the same length. However, the truncation/padding causes information distortion because some information is lost and/or unnecessary information is added, which can degrade the performance of text-dependent speaker verification. In this paper, we propose a method to handle variable length sequences for RNNs without adding information distortion by truncating the output sequence so that it has the same length as corresponding original input sequence. The experimental results for the text-dependent speaker verification task in part 2 of RSR 2015 show that our method reduces the relative equal error rate by approximately 1.3% to 27.1%, depending on the task, compared to the baselines but with an associated, small overhead in execution time. p 1 4 June 2020222 May 202011 June 20202011 June 202022 May 20202011 June 2020202011 June 20  
distortion, because short keywords are considered completely different even if they are only slightly changed. For example, the utterance of \"Hi Bixby\" and that of \"Hi Bixb\" are considered different classes of utterance in TD-SV, even if these utterances are from the same speaker. Therefore, it is important to model short utterances with minimization of information distortion. RNNs can effectively model not only speaker information but also phrase information in utterance. In TD-SV, the RNN in a speaker-and-phrase-discriminant network is used for mapping variable-length utterance to a fixed-size vector (this fixed-size vector is usually called embedding). The embedding is expected to represent both speaker and phrase information in the utterance. It can be either the output frame from the last time step or part of temporal pooling (e.g., running average along time axis) of the output sequence. In this paper, we focus on the latter method, temporal pooling, because it has been empirically confirmed that temporal pooling has higher performance [2]. Like other kinds of neural networks, RNNs are also trained in mini-batch units. To feed a set of input sequences into an RNN in mini-batch units, all the sequences in a given mini-batch must be the same length (denoted as the target length). In most cases, however, input sequences have variable lengths, furthermore, we do not know the length of the sequences in advance. In general, truncation/padding is used to make sure all variable-length sequences have the same target length. Based on the target length, the longer sequences are truncated, and the shorter sequences are padded. Note that there is no specific criterion for setting the target length, and the target lengths are different on a case by case basis. However, truncation/padding cannot avoid information distortion of the original sequences (i.e., sequences that have not been truncated/padded), because truncation brings a loss of some existing information, and padding brings additional unnecessary information not in the original. This problem may degrade the performance of TD-SV, especially when utterances are very short (i.e., short keyword-based TD-SV). Because the short utterance is vulnerable to information distortion, as mentioned above. Sequential processing is an alternative way to avoid this information distortion: feed only one input sequence at a time (i.e., the size of mini-batch is 1), repeat the feeding for all given sequences, and then gather the outputs over the mini-batch. Nonetheless, we do not consider sequential processing in this paper, it is too inefficient in terms of computation speed.