Speech Segmentation Optimization using Segmented Bilingual Speech Corpus
for End-to-end Speech Translation
Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
Nara Institute of Science and Technology, Japan{fukuda.ryo.fo3, sudoh, s-nakamura}@is.naist.jp

Abstract
Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD
tools like WebRTC VAD1 have generally relied on pause-based
segmentation. Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connectedby a very short pause that is difficult to detect by VAD. In thisstudy, we propose a speech segmentation method using a binary classification model trained using a segmented bilingual
speech corpus. We also propose a hybrid method that combines
VAD and the above speech segmentation method. Experimental results reveal that the proposed method is more suitable forcascade and end-to-end ST systems than conventional segmentation methods. The hybrid approach further improves the translation performance.
Index Terms: speech translation, segmentation, bilingual
speech corpus

1. Introduction
The segmentation of continuous speech is one process required
for speech translation (ST). In text-to-text machine translation(MT), an input text is usually segmented into sentences using punctuation marks as boundaries. However, such explicit
boundaries are unavailable in ST. Pre-segmented speech in abilingual speech corpus can be used when training an ST model,
but not in realistic scenarios. Since existing ST systems cannot
directly translate long continuous speech, automatic speech segmentation is needed.
Pause-based segmentation using voice activity detection(VAD) is commonly used for segmenting speech for preprocessing automatic speech recognition (ASR) and ST, even thoughpauses do not necessarily coincide with semantic boundaries.
Over-segmentation, in which a silence interval fragments a sentence, and under-segmentation, in which multiple sentences areincluded in one segment while ignoring a short pause, are problems that reduce the performances of ASR and ST [1].
In this study, we propose a novel speech segmentation
method2 based on the segment boundaries in a bilingual speech
corpus. ST corpora usually include bilingual text segment pairs
with corresponding source language speech segments. If we
can similarly segment input speech to such ST corpora, wemight successfully bridge the gap between training and inference. Our proposed method uses a Transformer encoder [2]that was trained to predict segment boundaries as a frame-level
sequence labeling task for speech inputs.
The proposed method is applicable to cascade and end-toend STs because it directly segments speech. Cascade ST is a
1 https://github.com/wiseman/py-webrtcvad
2 There is an independent work by Tsiamas et al., (2022)(https://arxiv.org/abs/2202.04774).

traditional ST system that consists of an ASR model and a textto-text MT model. An end-to-end ST uses a single model todirectly translate source language speech into target language
text. We limit our scope to a conventional end-to-end ST, which
treats segmentation as an independent process from ST. Integrating a segmentation function into end-to-end ST is futurework.
We conducted experiments with cascade and end-to-end
STs on MuST-C [3] for English-German and English-Japanese.
In the English-German experiments, the proposed method
achieved improvements of -9.6 WER and 3.2 BLEU for cascade ST and 2.7 BLEU for end-to-end ST compared to pausebased segmentation. In the English-Japanese experiments, theimprovements were -9.6 WER and 0.5 BLEU for cascade ST
and 0.6 BLEU for end-to-end ST. A hybrid method with theproposed segmentation model and VAD further improved bothlanguage pairs.

2. Related work
Early studies on segmentation for ST considered modeling with
the Markov decision process [4, 5], conditional random fields[6, 7], and support vector machines [8, 9, 10, 11]. They focused on cascade ST systems that consist of an ASR model anda statistical machine translation (SMT) model, which were superseded by newer ST systems based on neural machine translation (NMT).
In recent studies, many speech segmentation methods based
on VAD have been proposed for ST. Gaido et al. [12] andInaguma et al. [13] used the heuristic concatenation of VAD
segments up to a fixed length to address the over-segmentation
problem. Gállego et al. [14] used a pre-trained ASR model
called wav2vec 2.0 [15] for silence detection. Yoshimura et al.
[16] used an RNN-based ASR model to consider consecutive
blank symbols (“ ”) as a segment boundary in decoding using
connectionist temporal classification (CTC). Such CTC-based
speech segmentation has an advantage; it is easier to intuitively
control segment lengths than with a conventional VAD because
the number of consecutive blank symbols that is considered aboundary can be adjusted as hyperparameters. However, thesemethods often split audio at inappropriate boundaries for ST
because they mainly segment speech based on long pauses.
Re-segmentation using ASR transcripts is widely used incascade STs. Improvements in MT performance have been reported by re-segmenting transcriptions to sentence units using
punctuation restoration [7, 11, 17, 18, 19] and language models[20, 21]. Unfortunately, they are difficult to use in end-to-end
ST and cannot prevent ASR errors due to pause-based segmentation. We discuss ASR degradation due to speech segmentation
with VAD in section 5.1.
Finally, we refer to speech segmentation methods based on
segmented speech corpora that are more relevant to our study.

utterance ID

Start

End

ted_01_001 12.61
ted_01_002 16.90
ted_01_003 22.53
ted_01_004 31.52
ted_01_005 33.34

16.68
22.04
30.63
33.07
37.49

[ "! "" "# "$]
16.90

22.04

22.53

30.63

[ "!! "!" "!# "!$]

000 … 00011. . . 11000. . . 000
22.53

30.63

31.52

Linear + Softmax

33.070000 … 000011. . . 1100. . . 00

Transformer encoder𝑥 ∈ {0, 1} (×number of frames)

2D convolution

Figure 1: Data extraction examples from MuST-C: Labels 0 and
1 are assigned to each FBANK frame based on each segment’s
starting and ending times.
Wan et al. [1] introduced a re-segmentation model for modifying the segment boundaries of ASR output using movie andTV subtitle corpora. Wang et al. [22] and Iranzo-Sánchez et al.
[23] proposed an RNN-based text segmentation model using thesegment boundaries of a bilingual speech corpus. Unlike thesemethods that require ASR transcripts, our proposed method directly segments speech and can be used in an end-to-end ST as
well as in a cascade ST.

Figure 2: Transformer encoder-based speech segmentation
model
where ws is a hyperparameter that adjusts the weights of
the unbalanced labels. Since most of the labels are 0 (withinutterances), we increase the weight on the loss of label 1 (outside utterances). We set ws = 0.9 based on the best loss of thevalidation data in a preliminary experiment.
3.4. Inference

3. Proposed method
Our method defines speech segmentation as a frame-level sequence labeling task of acoustic features. This section describes
the following details of it: the process that extracts training data
for the speech segmentation task from the speech translation
corpus (3.1), the architecture of the proposed model (3.2), andthe training (3.3) and inference algorithms (3.4).
3.1. Data extraction

During inference, speech is segmented at a fixed-length T and
input independently into the segmentation model. Fixed-length
segments are then resegmented according to the labels predictedby the segmentation model. The segmentation model simply
selects label ln ∈ {0, 1} with the highest probability at eachtime n:ln := arg max(xn ).
(2)
3.4.1. Hybrid method

We use a bilingual speech corpus that includes speech segmentsaligned to sentence-like unit text as training data for speech segmentation from continuous speech into units suitable for translation. Data extraction examples are shown in Fig. 1. Twoconsecutive segments are concatenated and assigned a label,x ∈ {0, 1}, representing that the corresponding frame is inside(0) and outside (1) of the utterance.
3.2. Segmentation model
The architecture of the proposed speech segmentation model isillustrated in Fig. 2. It consists of a 2D convolution layer, Transformer encoder layers, and an output layer (Linear+Softmax),which outputs label probability x̂n ∈ R2 at the n-th frame of
the convolution layer. The convolution layer reduces the sequence length by a quarter to handle long speech sequences.
Downsampling is applied to teacher label x to align its lengthwith the model’s output. Here, labels are simply extracted atregular time intervals calculated from the input-output length
ratio.
3.3. Training
The model is trained using the data extracted in section 3.1 andlearns to minimize cross-entropy loss Lseg (x̂, x) between prediction x̂ and label x:N Xexp(x̂n,1 )xn,1Lseg (x̂, x) := −ws logexp(x̂
n,0 + x̂n,1 )
n=1,exp(x̂n,0 )+ (1 − ws ) log
xn,0
exp(x̂n,0 + x̂n,1 )

ℒ %&'

(1)

In addition, to make the predictions more appropriate, we combined the model predictions calculated in Eq. (2) with the results of the VAD tool:arg max(xn ) ∧ vadn (len(n − 1) < maxlen)
ln :=arg max(xn ) ∨ vadn (len(n − 1) ≥ maxlen),(3)
where vadn ∈ {0, 1} is the VAD output at time n, corresponding to the active and inactive frames. Segment length len(n) attime n is denoted as follows:len(n) :=len(n − 1) + 1
0(ln = 1),(ln = 0)(4)

where len(0) := 0. Eq. (3) implies that a frame can only
be included in a segment boundary when segmentation modeland VAD agree. This agreement constraint may result in very
long segments, so we relax it not to require the agreement oncethe segment length exceeds maxlen. In that case, a frame canbe included in a segment boundary if either the segmentation
model or the VAD allows it.

4. Experimental setup
4.1. Tasks
We conducted experiments with English to German and English
to Japanese ST. We used MuST-C v1 for the English-German
and v2 for the English-Japanese experiments. Each dataset consisted of triplets of segmented English speech, transcripts, andtarget language translations. The English-German and EnglishJapanese datasets contained about 230k and 330k segments. As

Table 1: Model settings: †version 0.10.3
ASR

Epochs (epochs)
Encoder layers (elayers)
Decoder layers (elayers)
FNN dimensions (eunits, dunits)
Attention dimensions (adim)
Attention heads (aheads)
Mini-batch (batch-size)
Gradient accumulation (accum-grad)
Gradient clipping (grad-clip)
Learning rate (transformer-lr)
Warmup(transformer-warmup-steps)
Label smoothing (lsm-weight)
Attention dropout (dropout-rate)

45

ST

MT
6

6
2048
256
4
64
2
5
5
2.5

BLEU

100
12

16

WER

Settings (ESPnet† options)Fixed-length (BLEU ) 40
Best VAD (BLEU )Fixed-length (WER )35
Best VAD (WER )

18

30

14

25

12
96
1
1

25000
0.1
0.1

acoustic features, we used 83-dimensional vectors consistingof an 80-dimensional log Mel filterbank (FBANK) extracted
by Kaldi3 and 3-dimensional pitch information. We preprocessed the text data with Byte Pair Encoding (BPE) to split
the sentences into subwords with SentencePiece [24]. A dictionary with a vocabulary of 8,000 words was shared between
the source and target languages.
To evaluate the performance, we aligned the outputs of amodel for automatically segmented speeches by each segmentation method (section 4.3) to the reference text with an edit
distance-based algorithm [25]. We then calculated WER for
ASR and BLEU for MT and ST.
4.2. ST systems
We used the Transformer implementation of ESPnet4 [26, 27]
to build the ASR and MT models for the cascade ST and an
ST model for the end-to-end ST. For the ASR and ST models using acoustic features as input, we added a 2D-convolution
layer before the transformer encoder layers. The model settings are shown in Table 1. We trained the ASR model withhybrid CTC/attention [28] that incorporates the CTC loss into
the Transformer. The weight on the CTC loss was set to 0.3.
The ST model parameters were initialized with the encoder ofthe ASR model. The parameters of each model were stored foreach epoch. After training to the maximum number of epochs,
we averaged the parameters of five epochs with the highest validation scores for evaluation.
4.3. Segmentation methods
4.3.1. Baseline 1: VAD
As a baseline, we used WebRTC VAD, a GMM-based VAD.
We tried nine conditions in the range of Frame size={10, 20, 30ms} and Aggressiveness={1, 2, 3}.
4.3.2. Baseline 2: fixed-length
Fixed-length is a length-based approach that splits speech ata pre-defined fixed length [5]. Although over- and undersegmentations are likely to occur because the method does not

20
4

8

12

16

20

24

28

Fixed-length (seconds)

32

36

Figure 3: Baseline scores on English-German cascade ST: Vertical axis indicates BLEU (left) and WER scores (right) for ASR
and MT models.
take acoustic and linguistic clues into account, its advantage isthat the segment length was kept constant. We tried ten fixedlength settings with parameters ranging from 4 to 40 seconds at
4-second intervals.
4.3.3. Speech segmentation optimization
We performed speech segmentation using our proposed model
described in section 3.2. During training, two consecutive segments were concatenated, as described in section 3.1. The
model’s setting was almost identical as that of the ASR encoder
shown in Table 1. However, since the speech segment concatenation roughly doubles the average length of the input, we set
the number of mini-batches to 32 and the number of gradientaccumulations to 4. Fixed-length T of the input during the inference was set to 20 seconds based on the best score of thevalidation data.
4.3.4. Hybrid method
We also performed the hybrid method with our model and VAD,
as introduced in section 3.4.1. For the hybrid method, we used
a WebRTC VAD with (frame size, aggressiveness) = (10 ms, 2)and set maxlen to ten seconds.

5. Results and discussion
5.1. Baselines
Figure 3 shows the ASR and MT results by cascade ST for each
fixed-length. For comparison, the scores for the setting with thehighest BLEU of VAD are shown as straight lines (Best VAD).
With the fixed-length approach, WER and BLEU improved inproportion to the input length and deteriorated after reaching acertain length. This result suggests that longer segments canprevent the degradation of the ASR and ST performances due
to automatic segmentation. On the other hand, we identified an
upper limit to the segment length that the model successfully
handled, which depends on its capability. Therefore, automatic
speech segmentation is important to prevent ST performancedegradation. In addition, the best VAD results were worse thanthe best fixed-length results (Best Fixed-length)5 , suggestingthat over- and under-segmentation due to pause-based segmentation significantly reduced the ASR and ST performances.

3 https://github.com/kaldi-asr/kaldi
4 https://github.com/espnet/espnet

40

5 A similar trend was shown in a previous study [12].

Table 2: Results measured in WER and BLEU for tst-COMMON
on MuST-C English-German cascade and end-to-end ST.

OracleBest VAD
Best Fixed-length
Our model+VAD hybrid

Cascade ST
WER BLEU
12.60
23.59
30.59
17.02
20.60
19.29
20.99
20.18
19.06
20.99

End-to-end ST
BLEU
22.50
16.40
17.96
19.10
19.87

Table 4: Example of ASR and MT outputs with segmentation
positions: ■ indicates segment boundaries.
Oracle (ASR)

Oracle (MT)Best VAD (ASR)

Table 3: Results measured in WER and BLEU for tst-COMMON
on MuST-C English-Japanese cascade and end-to-end ST.

Best VAD (MT)Cascade ST
WER BLEU
9.30
12.50
25.81
9.26
18.89
9.64
16.21
9.71
13.67
10.60

Our model (ASR)

OracleBest VAD
Best Fixed-length
Our model+VAD hybrid

End-to-end ST
BLEU
10.60
8.14
8.52
8.77
9.24

Our model (MT)bonobos are together with chimpanzees you aposre living closest relative ■Bonobos sind zusammen mit Schimpansen, Sie leben am nachsten Verwandten. ■bonobos are ■ together with chimpanzees you aposre living closest relative that ...
Bonobos sind es. ■ Zusammen mit
Schimpansen leben Sie im Verhaltnis
zum ...
bonobos are together with chimpanzees you aposre living closest relative ■Bonobos sind zusammen mit Schimpansen, Sie leben am nachsten Verwandten. ■Our model

5.2. Proposed method
Table 2 shows the overall results of the English-German experiments. Our model outperformed VAD and the fixed-length
segmentations for both cascade and end-to-end STs. Roughly
speaking, there are improvements in +3 BLEU for the Best VAD
and in +1 BLEU for the Best Fixed-length. This suggests that
our model can split the speech into segments that correspond tosentence-like units suitable for translation.
In addition, the hybrid method with VAD significantly improved both cascade (-1.93 WER and +0.81 BLEU) and endto-end STs (+0.77 BLEU). We confirmed that a hybrid methodwith our model and VAD greatly improved the translation performance. However, room for improvement remains comparedto the oracle segments contained by the MuST-C corpus.
Table 3 shows the overall results of the English-Japaneseexperiments. They resemble those in English-German; ourmodel outperformed the existing methods, and a hybrid withVAD achieved more significant improvements. We conclude
that the proposed method is also effective for distant language
pairs.
5.3. Case study
Table 4 shows an example of the ASR and MT outputs ofthe cascade ST using VAD and our segmentation model. In
VAD, more over- and under-segmentation occurred comparedto the oracle segments. In the example, the best VAD resulted
in over-segmentation (“bonobos are ■ together”) and undersegmentation (“relative that ...”). These errors caused differences in the oracle segments in the MT results. On the other
hand, our model split the speech at a boundary close to an oracle segment and obtained the same translation results.
Figure 4 shows an example of the waveforms and their segmentation positions. The top two waveforms show that ourmodel and VAD caused over-segmentation. As the bottom
waveform shows, hybrid decoding split the audio at a boundary near the oracle and alleviated the problem by requiring anagreement between our model and the VAD.

VAD

Our model +VAD hybrid
28.36 s36.79 sOracle segments
Predicted segments

Figure 4: Visualization of waveforms and segmentation positions

6. Conclusions
We proposed a speech segmentation method based on a bilingual speech corpus. Our method directly split speech into
segments that correspond to sentence-like units to bridge thegap between training and inference. Our experimental results
showed the effectiveness of the proposed method compared toconventional segmentation methods on both cascade and endto-end STs. We also demonstrated that combining the predictions of our model and VAD further improved the translation
performance.
Future work will integrate a segmentation function into an
end-to-end ST. In this study, we treated segmentation as an independent process from ST, although it should be included in
the end-to-end ST. We will investigate ways to integrate our
proposed segmentation method into ST for streaming or online
processing. Future work will also further investigate different
domains, other language pairs, and noisy environments and improve the segmentation model to reduce the need for VAD.

7. Acknowledgements
Part of this work was supported by JST SPRING Grant
Number JPMJSP2140 and JSPS KAKENHI Grant Numbers
JP21H05054 and JP21H03500.

18

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