PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Detection of anchors’ utterances in broadcast news using
i-vector-based speaker similarity and temporal information
Daichi NOZAKI; Masaru YAMASHITA; Hiroyuki TAKADA; Shoichi MATSUNAGA
Nagasaki University, Japan

ABSTRACT
Accurate speech recognition of anchors’ speech in broadcast news programs is essential for indexing tasks
such as topic extraction, summarization, and so on. Consequently, the number of anchors and all speechsegments for each anchor are important aspects that must be evaluated correctly. This paper discusses theeffectiveness of these two items based on speaker similarity using i-vector. In our proposed approach, first,
each input audio source was segmented into four types of sound: speech, noise, silence, and music. Next, aclustering was performed using all speech segments to collect segments from the same speakers. In thisclustering, cosine similarity from i-vector was used. However, it was difficult to extract sufficient speakercharacteristics from short utterances. To address this problem, we considered the time intervals between
successive pairs of speech segments and the existence of background noises, adding to the information
obtained from i-vector. Among the detected speaker clusters, anchor clusters were obtained based on theheuristic that the ratio of the number of speech segments from anchors was much higher than that of otherspeakers. It was experimentally shown that the detection method of anchors’ speech using the proposed
approach outperformed the method using conventional k-means clustering.
Keywords: Anchor, Speech, I-vector, Clustering

1. INTRODUCTION

Today, consumers of multimedia demand increasing access to multimedia out of the wide array ofavailable contemporary and archival content. A typical example of such content is broadcast news.
Accessing this content effectively requires useful descriptions (referred to frequently as metadata),
which are made based on the verbal information extracted from speech and text data, including
nonverbal sound and visual information. For the producer, however, creating metadata manually isexpensive and time-consuming. Automatic speech recognition, speaker detection, and audio source
detection including voice activity detection, are possible solutions to these problems. With thedevelopment of machine learning techniques, state-of-the-art technologies in these areas haveincreased recognition and detection performance drastically. Unfortunately, the presence of various
competing sound sources common to most multimedia content significantly degrades the performanceof metadata producing algorithms
Recently, many studies have been conducted on indexing multimedia content for information
retrieval, focusing on areas such as topic detection and summarization [1-3]. Our paper focuses on theefficient indexing of broadcast news using audio information. In broadcast news content, speech
comes from many sources such as anchors, commentators, interviewers, and interviewees. We focusedon what anchors said because anchors direct the flow of topics as such include most of the relevant
keywords for the development of metadata. In our research, we assumed that there was no preexisting
information regarding the anchors’ acoustic information or the number of anchors, instead relying on
general assumptions about the timing and environment around their speech. Generally, we expect thatthe proportion of anchors’ speech to the total amount of speech in a broadcast is much higher than any
other speakers. This allows us to separate their speech from other speakers by performing voiceactivity detection based on the frequency of speech and similarity to other sounds from the speaker. If
some clusters consist of speech segments which seem to be spoken by the same speaker, and theamount of the speech segments is proportionally great, we can assume that a single individual said
those speech segments and that the speaker is an anchor. Likewise, these patterns of speech can be used
to identify different anchors within a broadcast.
I-vector has been widely used for speaker verification and recognition [4, 5]. We have used it in our

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study to detect and assign speech to individuals. Detection of long segments of speech was
successfully carried out; however, for shorter segments, the detection rates became lower, because it
was difficult to extract sufficient speaker characteristics [6-8]. In broadcast news programs, there areseveral phrases, which although are short, vital for indexing.
To address this problem, we specifically focused on short time intervals between successive
segments of speech and the lack of background noise and combined these with the informationacquired from the i-vector. Trained anchors in a studio environment use the cadence of their speech
and a quiet background to enhance the listening environment for the audience [9]. Through our
research of Japanese broadcast news, we found that the time between speech segments uttered by thesame individual is shorter than that between speech segments uttered by two people. If the timeinterval between successive segments of speech is short enough, these are connected into one longsegment and the i-vector is calculated again using this newly segment of sound. Another way to
distinguish anchors’ speech is to compare lack of background noise, because they usually speak in astudio. If background noise is not detected around the successive segments of speech and the i-vectorof one segment is similar to that of another segment, these can be added onto the segment of speech
and the i-vector re-calculated. In this paper, we evaluated the effects of these two methods for
improving i-vector on short segments of speech during the detection of anchors’ speech segments inbroadcast news content.

2. ANCHORS’ SPEECH IN BROADCAST NEWS
2.1 Manual Tagging for Audio Broadcast News Programs
We prepared tagged data for five broadcast news programs to evaluate the detection performance ofanchors’ speech. The length of each program was about 30 min, with either one (Data I) or two (Data
II-V) anchors. We manually tagged the data by segment according to the four types of sound sources:
speech, music, noise, and silence segments and determined the beginning and ending times for eachsegment.
Speech segments consisted of words from anchors, reporters, interviewees, and all othertranscribable speech. These segments included very short pauses, such as when the speaker took ashort breath. However, background voices, which were usually picked up from outdoor locations, were
tagged along with traffic and other general sound sources as noises. We identified individual speakers
with a numerical tag for each speech segment to compare how well our methods were able to detecteach anchor’s speech. Jingles were tagged as music. Some tagged segments overlapped, such as aninterview in a crowd, which would contain both speech and noise.
2.2 Detection Flow of Anchors’ Speech
Figure 1 illustrates how we detected anchors’ speech. First, we applied sound source segmentation
to the evaluation audio data and subsequently extracted speech segments. Then, we performed speech
clustering by finding the speaker similarities between speech segments to assign speech to individual
speakers based on how these assignments were clustered. Finally, clusters in which the ratio ofsegments to total speech segments was larger than the predefined thresholds were assigned to theanchors. By counting the number of clusters that exceeded the thresholds, we estimated the number ofanchors.
The sound source detection procedure detected four types of sound segments: speech, music, noise,and silence. We used seven acoustic features: signal energy, pitch frequency, peak-frequency centroid,peak-frequency bandwidth, spectral cross-correlation, temporal stability, white-noise similarity, andspectral shape [10,11]. In our speech detection procedure, we obtained the detection results for eachtimeframe for the four sound segment types and the likelihood being of each type for each featureparameter. The likelihood was calculated using the acoustic Gaussian mixture models (GMMs) of each
type. The results for each timeframe were smoothed using a longer window to avoid inaccurate
detection of very short segments. The details of this procedure are described in our reference [11].

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Figure 1 – Anchor speech detection procedure

2.3 Speaker Similarity using I-vector
To correctly assign speech segments to an individual speaker, we needed to exactly capture
speaker-dependent acoustic features in each segment of speech. To do this, we measured the differencein speaker features. We used i-vector to describe speaker characteristics and to measure the speakersimilarity between two segments of speech by using the cosine similarity between two segments’
i-vectors. This measure using i-vector usually achieves good performance for longer segments ofspeech but is inaccurate for shorter ones, as demonstrated in Table 1, which shows the mean value andstandard deviation of cosine similarity for long speech segments (> 20 s) and short speech segments(< 300 ms) using speech data read by 110 speakers in Japanese. Figure 1 also shows the distributionsof both lengths of speech segment. Importantly, there were few differences in the ratio of short speechsegments between different speakers and a single speaker. This indicates that information is needed todetect the anchors’ short speech segments in addition to the cosine similarity of i-vectors.
To address this problem, we focused on using the time interval between successive speech segmentsfrom a single speaker and different speakers. Table 2 shows the mean value and standard deviation for
two interval lengths in our tagged data. Their distributions are shown in Figure 2. These indicates thatthe interval between successive speech segments from the same speaker is generally shorter comparedto that between one speaker and the other. In this paper, we experimentally verify the effectiveness ofutilizing this information.
Another way in which the issue of i-vector can be solved for short lengths of speech is by using
background noise. In most cases, anchors work in a silent studio that typically results in low levelsof background noise. Higher levels of noise most often indicates that the person speaking is not ananchor. We experimentally evaluated this method in section 4.
Table 1 – Value of cosine similarity between read speech segments from same/different speakers
Long speech segments (> 20 s)Same speakers

Different speakers

Mean

0.91

S.D.

0.07

Short speech segments (< 300 ms)
Same speakers

Different speakers

0.00

0.12

0.03

0.31

0.33

0.32

Figure 2 – Cosine similarity distribution for long and short read speech segments

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Table 2 – Interval between a pair of successive speech segments (in sec)
IntervalSame speakers

Different speakers

Mean

0.68

2.44

S.D.

1.15

1.0

Figure 3 – Interval distributions between successive speech segments from the same speaker vs. different
speakers in broadcast news

3. DETECTION OF ANCHORS’ SPEECH
3.1 Detection of Speech from an Individual Speaker based on Speaker Similarity
After the detection of speech segments in the news content, we gathered speech segments fromindividual speakers based on speaker similarity. One of the most conventional methods to groupspeech is by using k-means clustering based on speaker-dependent characteristics, such as the i-vector.
As before, we found this method unsuitable due to the several short speech segments in which thei-vector did not prove to be good acoustic feature. Instead, we focused on the larger volume of anchor
speech as compared to other speakers. Our detection approach was to find the speech clusters in which
speaker characteristics for each speech segment were similar and in which the speech made up a larger
proportion of the total segments. Our approach to detect speech segment clusters is as follows:
Step 1: Calculate the i-vector based cosine similarity 𝐷 �𝐼(𝑢𝑖 ), 𝐼�𝑢𝑗 �� between all pairs of all longspeech segments �𝑢𝑖 , 𝑢𝑗 , 1 ≤ 𝑖, 𝑗 ≤ 𝑁�, where N is the number of the detected speech segments in thesound source detection.
Step 2: Determine the ratio 𝐶(𝑢𝑖 ) of speaker-similar speech segments for each speech segment 𝑢𝑖 .
The speaker-similar speech segment 𝑢𝑗 were selected as members in the cluster 𝑆(𝑢𝑖 ) using a

pre-defined threshold 𝐷𝑇ℎ1 : 𝐷 �𝐼(𝑢𝑖 ), 𝐼�𝑢𝑗 �� > 𝐷𝑇ℎ1 , where 𝑢𝑖 is a centroid in the cluster. If theratio 𝐶(𝑢𝑖 ) is larger than a pre-defined threshold 𝐶𝑇ℎ , the speech segments in this cluster wasrecognized as belonging to an anchor.
Step 3: Detect short segments of speech 𝑢𝑘 belonging to the cluster 𝑆(𝑢𝑖 ) if 𝐷�𝐼(𝑢𝑖 ), 𝐼(𝑢𝑘 )� >𝐷𝑇ℎ2 to compensate for insufficient i-vector for short segments of speech. 𝐷𝑇ℎ2 is also a
pre-defined threshold that must be larger than 𝐷𝑇ℎ1 .
Steps 2 and 3 were repeated for unselected speech segments for all segments until the ratio of6811

selected speech segments in a cluster in step 2 was less than the pre-defined threshold 𝐶𝑇ℎ . Finally,
the number of generated clusters was recognized as the number of anchors within the news
broadcast.
3.2 Speech Segment Connection for Accurate Detection of Anchors’ Speech
In this section we develop our baseline method for detecting distinct speech for individual anchors.
In this baseline, we perform the clustering process for short speech segments as described in step 3,
however, this process is not sufficient to deal with all short speech segments. As a result, we tried toderive more reliable i-vector value by connecting successive segments of speech and building upon thealgorithm described in section 3.1 using temporal information and background noise information withthe following three methods:
Method I (temporal information): If the time interval between successive speech segments is lessthan the pre-defined time-length, and cosine similarity of i-vectors of these segments is also larger
than the pre-defined threshold, these segments are connected and i-vector is re-obtained using the
newly combined segment.
Method II (background noise information): If all neighboring acoustic segments to the successive
speech segments are silence, as shown in Figure 4, and the cosine similarity of i-vectors of thesegments is also larger than the pre-defined threshold, these segments are connected and i-vector is
re-obtained using the combined segment.
Method III (both types of information): Methods I and II are combined by connecting the pair of
successive segments if either condition (time interval length or silence) is met.

Figure 4– Recalculation of i-vector for successive speech segments in method II

4. Evaluation Experiments
4.1 Experimental Conditions
We used five broadcast news programs as described in section 2.1 for evaluation. In this data,speech and silence segments accounted for about 60% and 15% of the total audio, respectively. In thesound source detection process, the sampling rate of the audio data was 44.1kHz. Every 23 ms, theseven acoustic feature parameters in section 2.2 were determined using a 46-ms Hamming window.
The acoustic GMM with a two-mixture Gaussian distribution was used for each sound source. The
speech detection performance (F-measure) of this sound source detection process was 89.3%.
In the speech cluster detection process, acoustic features under 8 kHz were used to capture speakercharacteristics. I-vector dimensionality was 10, and universal background model (UBM) size was 60.
The UBM was trained using 50 sentences read by 55 male and 55 female speakers.
The thresholds (𝐷𝑇ℎ1, 𝐷𝑇ℎ2 ) for cosine similarity and that for the ratio of the number of speechsegments 𝐶𝑇ℎ to the overall speech segments was determined experimentally.

4.2 Detection Performance
Finally, we conducted detection experiments of anchors’ speech from two points of view: speechsegment clustering and speech segment connection. Through the lens of speech segment clustering, we
compared the conventional k-means clustering based on the speaker-similarity between speechsegments to the proposed clustering method as described in section 3.1. From the perspective ofspeech segment connection, the detection experiment without connecting speech segments (ourbaseline) was compared to methods I,II, and III, which connected speech segments based on the timeinterval, background silence, and both, respectively.

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Table 3 shows the detection performance of each method. The detection performance (F-measure)of the baseline was higher than that of the k-means clustering. This indicates that clustering based on
the ratio of anchor speech to total speech, grouped by speaker similarity, is more effective. Table 3 also
shows that the performances of methods I, II, and III were superior to that of the baseline, and method
I achieved the highest performance. These results show that the connection of speech segments prior tothe clustering of speech segments, creating longer segments of speech to cluster, making it possible tomore reliably obtain i-vector and more accurately detect an individual anchors’ speech. It also showsthat connecting speech segment based on background silence is useful, but not as much whenconnecting speech based solely on time interval between speech segments.
Every detection method except for k-means clustering correctly identified the number of anchors,
however more evaluation data is needed to analyze their performance more thoroughly.
Table 4 shows the detection performance of anchors’ speech for each broadcast analyzed. These
results indicate that the speech detection and assignment in the case of two simultaneously reporting
anchors in a broadcast is more difficult than that of one anchor.
Table 3 – Detection performance of anchors’ speech
MethodRecallPrecision

F-measurek-means clustering

0.961

0.495

0.645

Baseline

0.748

0.670

0.707

Method I

0.747

0.798

0.765

Method II

0.737

0.722

0.730

Method III

0.733

0.776

0.754

Table 4 – Detection performance of anchors’ speech for each broadcast
Evaluation news

No. of anchors

F-measure

Data I

1

0.815

Data II

2

0.722

Data III2

0.789

Data IV

2

0.711

Data V2

0.765

5. CONCLUSIONSThis paper investigated the detection methods for effectively isolating and assigning individual
news anchor’s speech to automatically index broadcast news programs. In our approach, speech froman individual speaker was collected using the cosine similarity of i-vector for each segment of speech.
News anchor’s speech was separated from the speech of other speakers by establishing that the ratio of
anchor’s speech to total speech was higher than that for any other speakers. Since using i-vector on
short speech segments is ineffective due to the lack of sufficient speaker characteristics, we connectedsuccessive pairs of speech segments into one speech segment. To do this, we used both the timeintervals between speech segments and the lack of background noises, then added this to theinformation obtained from i-vector. We connected these shorter speech segments prior to clustering
them to individual anchors to use more reliable i-vector for each segment of speech. The experimental
results showed that the clustering method which considered the ratio of anchor speech to overall
speech was effective. We also obtained useful results by connecting speech segments based on the timeinterval between segments.
Due to the time-consuming nature of this experiment, the quantity of data analyzed was small andfurther research is required to validate our findings. Since the thresholds used in our methods were
determined experimentally, these may also benefit from refinement with larger data sets.

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