PACLIC 24 Proceedings

PodCastle: A Spoken Document Retrieval Service Improved byAnonymous User Contributions
Masataka Goto and Jun Ogata
National Institute of Advanced Industrial Science and Technology (AIST)
1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, JAPANm.goto [at] aist.go.jp

Abstract. In this invited paper, we introduce a public web service, PodCastle, that provides full-text searching of speech data (Japanese podcasts) on the basis of automatic speech
recognition technologies. This is an instance of our research approach, Speech Recognition
Research 2.0, which is aimed at providing users with a web service based on Web 2.0 sothat they can experience state-of-the-art speech recognition performance, and at promoting
speech recognition technologies in cooperation with anonymous users. PodCastle enables
users to find podcasts that include a search term, read full texts of their recognition results,
and easily correct recognition errors by simply selecting from a list of candidates. Even if
a state-of-the-art speech recognizer is used to recognize podcasts on the web, a number of
errors will naturally occur. PodCastle therefore encourages users to cooperate by correcting
these errors so that those podcasts can be searched more reliably. Furthermore, using theresulting corrections to train the speech recognizer, it implements a mechanism whereby the
speech recognition performance is gradually improved. In our experiences from its practicaluse over the past 46 months (since December, 2006), we confirmed that the performance of
PodCastle was improved by a number of anonymous user contributions.
Keywords: information retrieval, speech recognition, error correction, wisdom of crowds,
Web 2.0

1 Introduction
Speech recognition researchers understand what sort of speech is easily recognized by speech recognizers and realize that speech recognizers perform best when dealing with clean speech. On theother hand, most end users of speech recognizers judge the effectiveness of speech recognition
from their limited experiences and do not necessarily understand how useful state-of-the-art recognizers can be. Users sometimes do not adequately comprehend what sort of voices or recording
conditions make recognition difficult. If they have previously had difficulty being understood byspeech recognizers, they often doubt the usefulness of speech recognition and may stop using it.
The first aim of this study is to address this problem by promoting the popularization and useof speech recognition by raising end user awareness of state-of-the-art speech recognition performance. For this purpose, we launched a podcast search web service called PodCastle (Gotoet al., 2007; Ogata et al., 2007; Ogata and Goto, 2009b; Ogata and Goto, 2009a) in 2006 thatallows anonymous users to search and read podcasts, and to share the full text of speech recognition results for podcasts. Podcasts are audio programs distributed on the web, like radio shows oraudio blogs. They are becoming increasingly popular because updated podcasts (MP3 audio files)
can be easily and frequently downloaded by using RSS syndication feeds. Since various contents
have already been published as podcasts, users can grasp the current state of speech recognition
technology just by seeing the results of speech recognition applied to published podcasts. This is

Copyright 2010 by Masataka Goto and Jun Ogata

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Invited and Plenary Talks

important because when some users experience recognition errors while speaking into a microphone, they may become uncomfortable or frustrated and lose their motivation. Such problems do
not occur for PodCastle because users do not have to provide their own speech input at all.
However, even state-of-the-art speech recognizers cannot correctly transcribe all podcasts, because their contents and recording environments vary very widely. A typical approach to deal withspeech contents that cannot be properly recognized is to create a speech corpus including suchcontents and prepare correct transcriptions to train speech recognizers. This approach, however,is impractical for PodCastle because advance preparation of a corpus covering diverse podcast
contents will be too costly and time consuming.
The second aim of this study is to dispense with the idea of using a pre-prepared corpus toaddress this problem, and instead employ the efforts of a large number of users to improve speech
recognition and full-text search performance. Even if a state-of-the-art speech recognizer is used
to recognize podcasts on the web, a number of errors will naturally occur. PodCastle therefore encourages users to cooperate by correcting these errors so that those podcasts can be searched morereliably. Furthermore, using the resulting corrections to train the speech recognizer, it implements
a mechanism whereby the speech recognition performance is gradually improved. This approach
can be described as collaborative training for speech recognition.
In 2006, we coined the term Speech Recognition Research 2.0 (Goto et al., 2007) to refer tothe research approach where the current state of speech recognition technology is intentionallydisclosed to users so that speech recognition performance can be improved through cooperative
participation by users. This term was chosen to reflect the concept of Web 2.0 (O’Reilly), since
this approach brings the benefits of Web 2.0 to speech recognition research. In Section 2 of thispaper, we discuss the research approach that Speech Recognition Research 2.0 represents, and in
Section 3 we describe the PodCastle web service as an instance of this approach. In Section 4, wesummarize the contributions of this research.

2 Speech Recognition Research 2.0
Speech Recognition Research 2.0 is a new research approach to speech recognition which aims at
improving speech recognition performance and the usage rate while benefiting from the cooperation of a number of anonymous end users. To achieve this, we proposed setting into motion apositive spiral as explained in Figure 1. In the past, this spiral has not necessarily taken hold because of inhibiting factors in each of the three steps important for popularizing speech recognition.
The problems affecting each of these steps are as follows:• With regard to (i) understanding speech recognition performance, users have tended to see
the results of speech recognition applied only to their own voices. Once users experience
recognition problems with their voices, they tend to incorrectly assume that other people’s
voices will also not be well recognized. On the other hand, speech recognition researchers
have a better understanding of recognition capabilities because they have more opportunity
to see the results of speech recognition applied not only to their own voices but to a large
speech corpus.
• As for (ii) contributing to improved speech recognition performance, users of speech dictation systems can make speech recognizers adapt to their voices by reading out predefined
sentences or add out-of-vocabulary words.1 However, such in-house performance improvements made by each end user are not made available for re-use by other users. Only speech
recognition researchers have been able to improve the performance of speech recognition as
1

Some systems can adapt automatically to a speaker’s voice during use without the user’s awareness, and somesystems can automatically acquire out-of-vocabulary words. However, in neither of these cases it is possible to share
this information between users.

PACLIC 24 Proceedings(i) Allowing users to experience speech
recognition lets them better understandits performance.
(ii) Users contribute to improved speech
recognition performance.
(iii) Improved performance leads to a better
user experience.
Figure 1: A positive spiral leading towards greater use of speech recognition (through repetition of steps(i), (ii), and (iii)).
Table 1: A comparison of the conventional approach to speech recognition research (Speech Recognition
Research 1.0) and the proposed approach (Speech Recognition Research 2.0).
Speech Recognition Research 1.0 Speech Recognition Research 2.0
Stand-alone application
Web service
Dictation
Searching/browsing
CorpusWeb-based data
Limited topics
Unlimited topics
Transcription
Annotation
Out-of-vocabulary words
Not-yet-annotated words
Specialist participation
User participation
Individual correction
Social correction
Personal wisdom
Wisdom of crowds
Completed version
Perpetual beta
The above table is influenced by the comparison of Web 1.0 and Web 2.0 by
O’Reilly (O’Reilly). Research projects that feature more points on the rightside of the table are more worthy of the name Speech Recognition Research
2.0. However, as with Web 2.0, this does not mean that all these points haveto be featured in any one project.

a whole. Consequently, it has been difficult to motivate a number of users to contribute tocollaborative efforts that will improve performance.
• With regard to (iii) a better user experience, users have had little opportunity to experience
the better performance that results from ongoing improvements made by researchers. For
example, even if speech recognizers are made available as open source software (e.g., (Leeet al., 2001)), these are mainly aimed at developers; end users have little opportunity to
use them directly. Also, users of most speech recognition products have only experienced
performance improvements through infrequent software updates.
By addressing these problems, Speech Recognition Research 2.0 aims to change the usage ofspeech recognition by setting the positive spiral of Figure 1 into motion. Table 1 compares this approach with the conventional approach to speech recognition research, which we will call Speech
Recognition Research 1.0. We are not suggesting, though, that Speech Recognition Research 1.0
is inferior or obsolete, and there is no doubt that continued research using the Speech Recognition
Research 1.0 approach is needed. We ourselves have continued our work on Speech Recognition
Research 1.0 as the foundation for 2.0. It should also be stressed that we are discussing research
approaches, and not speech recognition techniques or algorithms themselves, which is why we use
the term Speech Recognition Research 2.0 instead of Speech Recognition 2.0.
In the following, we discuss how the beneficial spiral of Figure 1 can be put into effect while
also explaining the points in Table 1.

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Invited and Plenary Talks• Instead of developing stand-alone applications such as dictation or spoken dialogue by
preparing a corpus for speech recognizers, Speech Recognition Research 2.0 provides a web
service that allows users to search and browse open-to-the-public web-based speech datasuch as podcasts. In this way, it promotes understanding of speech recognition performance(step (i) in Figure 1).
• When recognizing web-based speech data, however, we cannot limit the range of topics andprepare in advance a suitable corpus with its transcription. This causes many recognition
errors, so Speech Recognition Research 2.0 gets users to correct the errors, thereby enablingrecognition of a wide variety of speech data on an unlimited range of topics. That is, users
cooperate in the preparation of full-text transcriptions as a form of annotation that can beused when searching for speech data. It is important that user corrections are also used fortraining speech recognizers so that not-yet-corrected errors in other parts or other speech data
can be reduced.2 In Speech Recognition Research 2.0, out-of-vocabulary words are regarded
as being nothing more than not-yet-annotated words which will be annotated (corrected)
by users and then automatically added to the system vocabulary. In this way, users cancontribute to improved performance (step (ii) in Figure 1).
• Furthermore, instead of confining this to individual corrections, we propose extending thisuser participation framework to provide a social correction framework, where a number ofanonymous users can improve the performance by sharing their correction results over a web
service. In this social framework, users gain a real sense of contributing to the convenienceof other users, and can be motivated to contribute by seeing the correction activities made byother users. In this way, we can use the wisdom of crowds to achieve a better user experience(step (iii) in Figure 1).
In other words, Speech Recognition Research 2.0 can be described as an approach whereby a
web service based on speech recognition that is permanently in beta version (perpetual beta) is
launched and then improved by inviting users to use it on the web, thereby advancing the research.
As the first instance of Speech Recognition Research 2.0, we initiated the PodCastle projectin January 2006. In this project, our goal is to set the positive spiral of Figure 1 into motion by
providing the PodCastle web service which is based on the concepts of both Web 2.0 and Speech
Recognition Research 2.0.

3 PodCastle: A Podcast Search Service Based on Speech Recognition
PodCastle is a social annotation web service where users can search, read, and annotate podcasts intext form. Each podcast consists of a series of episodes of audio data (MP3 files) and their metadata(RSS syndication feed) that promotes its circulation. The creator of a podcast (the podcaster) can
add new episodes at arbitrary intervals (daily, weekly, etc.). With RSS, updated episodes areautomatically downloaded from the web and can be stored in any type of player. Podcasts areoften referred to as audio blogs and their popularity has grown because anybody can publish anddownload audio data with ease. Just as full-text search services are essential for accessing text
web pages, there is a growing need for full-text speech retrieval services such as PodCastle.
Although there were previous research projects for speech retrieval (Whittaker et al., 1999;
Thong et al., 2002; Lee and Chen, 2005) (Cambridge Multimedia Document Retrieval Project;
CMU Informedia Digital Video Library Project) before 2006, most do not provide public web
services for podcasts. There were two major exceptions, Podscope (Podscope) and PodZinger(PodZinger), which started web services for speech retrieval targeting English-language podcasts
2

This is an original benefit of Speech Recognition Research 2.0 that is not provided by Web 2.0. For example, inother services such as Wikipedia (Wikipedia) based on the wisdom of crowds, the users’ contributions are limited tothe articles they edit. There is no automatic improvement of other articles.

PACLIC 24 Proceedings

in 2005. These services use speech recognition to turn podcasts into text, and can display a list of
podcasts that include a search term. In Podscope, users are shown none of the speech recognition
results and only the title list is provided, although speech data around the found search term canbe played back. In PodZinger, users are shown text excerpts (recognition results) surrounding thesearch term, allowing users to grasp the context of the podcast more easily. In contrast, PodCastle
is the first service to provide full-text searching of Japanese-language podcasts. Even if the otherservices can also support Japanese podcasts in the future, PodCastle differs significantly in threeways:
1. Although speech recognition has been used in earlier services, they have only displayed partsof the resulting text, making it impossible to visually ascertain the detailed contents of the
podcast without actually listening to it.
2. The full-text results of speech recognition have been hidden inside, so it has not been possible
to search them using other existing text-based search engines.
3. Even if users find that search results are degraded by unavoidable speech recognition errors,
the users have had no means of correcting these errors.
In contrast, PodCastle allows full-text results of speech recognition to be accessed by both users
and external search services, and allows a number of users to cooperate with each other to improve
the speech recognition performance.

3.1 Three Functions of PodCastle
PodCastle supports three functions — searching, reading, and annotating — to satisfy all the pointsof Speech Recognition Research 2.0 listed in Table 1 and set the positive spiral of Figure 1 into
motion. Specifically, the searching and reading functions let users better understand the speech
recognition performance regarding podcasts (step (i) in Figure 1), and the annotating (error correction) function allows them to contribute to improved performance (step (ii)). This improved
performance can then lead to a better user experience of searching and reading podcasts (step(iii)).
3.1.1 Searching Function This is a function that allows a full-text search of the speech recognition results (and the results corrected by users). When the user types in a search term, as with an
ordinary text-based search engine, a list of episodes containing this term is displayed together withtext excerpts of speech recognition results around the highlighted search term. These excerpts canbe played back individually. By selecting one of these search results, the user is then able to accessits full text by switching over to the reading function.
3.1.2 Reading Function With this function, as well as listening to a podcast the user can also
view the text of the podcast. This allows users to understand the contents of a podcast even when
audio playback is not possible, and allows them to quickly decide whether they are interested in
the podcast’s contents without having to listen to it. To make errors easy to discover, each word iscolored according to the degree of reliability estimated during speech recognition. Furthermore, acursor moves across the text in synchronization with the audio playback.
Because the full-text result of speech recognition being applied to each episode becomes available to external full-text search engines, such results can be discovered together with ordinary web
pages by these engines. This increases the value of podcasts by bringing more users into contact
with them. Since this benefits the podcasters, it will motivate them, together with other volunteer
users, to use the annotating function.
3.1.3 Annotating Function This function allows users to add “annotations” to correct any
recognition errors they may come across while searching or reading. Here, annotation means transcribing the podcast contents, either by selecting the correct candidate from the list of competitive

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Invited and Plenary Talks

Database manager
Speech recognition manager
Speechrecognizer
recognizer
SpeechSpeechrecognizer
Speechrecognizer
Speechrecognizer

Search engine

PodcastWeb crawler

Figure 2: PodCastle screen snapshot of an interface for correcting speech recognition errors (competitive
candidates are presented underneath the normal recognition results). Five errors in this excerpt were corrected by selecting from the candidates. The corrected Japanese sentence means “. . . well, actually the ratioof this price range and . . . ”.
Podcast(MP3+ RSS)
Podcast(MP3+ RSS)
Podcast(MP3+ RSS)(MP3 + RSS)

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User interface
User interface
User interface

Figure 3: Implementation overview of PodCastle.

candidates, or by typing in the correct text. For this purpose, we provide an efficient error correction interface we earlier proposed (Ogata and Goto, 2005). In this interface, shown in Figure 2,
a recognition result excerpt is shown around the cursor and scrolled in synchronization with theaudio playback. Each word in the excerpt is accompanied by other word candidates, which are
generated beforehand by using a confusion network3 (Mangu et al., 2000) that can condense ahuge internal word graph of a large vocabulary continuous speech recognition (LVCSR) system.
Note that users are not expected to correct all the errors, but can be expected to correct some errors
according to their interests.

3.2 Implementation of PodCastle
The implementation overview of PodCastle is shown in Figure 3. The web crawler collects podcasts, which can be added by users, and records them in the database manager. Those podcasts arethen recognized by multiple speech recognizers one after another. When a request from a speech
recognizer is received by the speech recognition manager, the next available episode to be recognized is handed over. After the recognizer finishes processing its episode, the recognition result
is passed to the database manager via the speech recognition manager. The database manager
controls the processing state of the podcasts and indexes their speech recognition results together
with the corrections provided by users. Finally, the search engine works as a website that provides
the PodCastle user interface with the three functions.
The web server of PodCastle was implemented by using a web application framework Ruby
on Rails 2.3.4, a programming language Ruby 1.8.7, a web server Passenger 2.2.11 and Apache
2.2.8, a database MySQL Enterprise 5.0.54a, a morphological parser for the Japanese language
ChaSen 2.3.3, and an embeddable full-text search engine Senna 1.1.1 and Tritonn 1.0.9. The clientinterface was implemented by using a scripting language JavaScript 1.5 and its library MochiKit
3

The confusion network was originally introduced in the context of word error minimization, which minimizes theword error rate of recognition results rather than the sentence error rate (Mangu et al., 2000). Our original idea
is to apply such an efficient intermediate recognition result to generate competitive candidates for efficient error
correction (Ogata and Goto, 2005).

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1.4, multimedia frameworks QuickTime 10 and Flash 10, and an ActionScript 2 compiler MTASC
1.12.
To recognize podcasts, audio data is first segmented into three categories — speech, music
without speech, and other background sounds — by applying GMMs. Speech segments are thenrecognized by using our in-house LVCSR system based on an efficient N-best search algorithm(Ogata and Ariki, 2000) to generate the confusion networks. This system uses cross-word tiedstate triphone HMMs trained for 39-dimensional MFCC-based features, and a 214k-word trigram
language model trained by using both large standard speech corpora and daily-updated web news.
We had to overcome various difficulties to achieve our speech recognizer for podcasts. In
terms of language modeling, for example, podcasts tend to include words and phrases related torecent topics, which are usually not registered in the system vocabulary. We therefore developed
a method to keep a language model up-to-date by using on-line news texts (Ogata et al., 2007).
In addition, in terms of acoustic modeling, podcasts include various types of speech data, such as
pure speech, noisy speech, narrow-band speech, and speech with music. To reduce the acoustic
mismatch, we apply several improvement methods such as noise suppression at the front-end anditerative unsupervised adaptation.
The automatic performance improvements through correction by users can be achieved throughvarious techniques, such as those for training an acoustic model by using corrected transcriptions,for making a language model adapt to different topics by using RSS metadata and corrected transcriptions, and for registering out-of-vocabulary words by using a phonetic typewriter to estimate
their pronunciation. The details of our speech recognizer are described in (Ogata et al., 2007;
Ogata and Goto, 2009b).

3.3 Experiences with PodCastle
PodCastle was released to the public at http://podcastle.jp on December 1st, 2006. PodCastle hasthen supported video podcasts in August 2009 by transcribing speech data in video podcasts anddisplaying an accompanying video screen in synchronization with the original PodCastle screen.
So far, 682 podcasts have been registered, consisting of 90,985 episodes in total (as of August 31,
2010). Of these, 2242 episodes have been at least partially corrected, which resulted in 481,948
corrected words (errors). Some podcasts registered in PodCastle were corrected almost everyday or every week. We found that there are users who voluntarily cooperate in the correction,
as happens with other Web 2.0 services, and that podcasts recorded by famous artists and TV
personalities tend to receive many corrections.
For the collaborative training of our speech recognizer, we introduced a podcast-dependent
acoustic model that is trained for each podcast using its transcripts corrected by anonymous users(Ogata et al., 2007; Ogata and Goto, 2009b; Ogata and Goto, 2009a). Through our experiments,
we confirmed that the speech recognition performance for some podcasts that received many error
corrections was actually improved by the acoustic model training (relative error reduction of 2133%) (Ogata and Goto, 2009b) and the burden of error correction was reduced for those podcasts.
Furthermore, we are currently studying and evaluating collaborative training of language models.
We have inferred some motivations for users correcting speech recognition errors on PodCastle, though we cannot directly ask since the users are anonymous. These motivations can be
categorized as follows:• Error correction itself is enjoyable and interesting
Since the error correction interface is carefully designed to be useful and efficient, using
it, especially for quick and accurate operations by proficient users, could be a form of fun
somewhat like a video game.
• Users want to contribute9

10Invited and Plenary Talks

Some users would often correct errors not only for their own convenience, but also to altruistically contribute to better speech recognition and retrieval.
• Users want their podcasts to be correctly searched
The creators of a podcast (podcasters) would correct recognition errors in their own podcast
so that it can be more accurately searched.
• Users like the content and cannot tolerate the presence of recognition errors in it
Some fans of famous artists or TV personalities would correct errors because they like the
podcasters’ voices and cannot tolerate the presence of recognition errors in their favorite
content. In fact, we have observed that such podcasts generally receive more corrections
than other types.
One Web 2.0 principle is to trust users and we also trust users with respect to the quality ofcorrection: in practice, the correction results obtained so far have been of high quality. Even
if some users deliberately make inappropriate corrections (vandalism), though, we can develop
countermeasures to acoustically evaluate the reliability of corrections. For example, we can use
the likelihood of HMMs for forced alignment with the corrections.

4 Conclusion
We have described the PodCastle web service which provides a search engine for podcasts on thebasis of the wisdom of crowds. This is the first instance of Speech Recognition Research 2.0 which
we have proposed as a new approach to speech recognition research that complements existing
approaches. The technical contribution of this study is to investigate how far the performanceof speech recognition and full-text search can be improved by getting speech recognition errors
corrected through the cooperative efforts of many end users. At the same time, it makes a social
contribution in that it helps web users by providing the world’s first public web service for full-text
search of Japanese-language podcasts.
Another contribution of this study is that it demonstrates how speech recognition can be put
to use in situations where a speech corpus is almost impossible to prepare in advance. Althoughspeech recognition usually requires a sufficient corpus to provide useful results, such corpora tend
to be costly and labor-intensive, thus limiting applications. On the other hand, this study has
aimed at collaborative training for speech recognition where full-text transcriptions containing
recognition errors are first disclosed and then corrected by anonymous users. Since there are manyerrors, we run the risk of attracting criticism, but we believe that sharing these results with users
will promote further popularization and use of speech recognition. We hope that this study will
prove the importance and potential of incorporating user contributions into speech recognition,and that various other projects that follow the Speech Recognition Research 2.0 approach will bedone, thus adding a new dimension to this field of research.

Acknowledgments
We thank Youhei Sawada, Shunichi Arai (Mellowtone Inc.), Kouichirou Eto (AIST), and Ryutaro
Kamitsu (Brazil Inc.) for their web service implementation. We also thank anonymous users of
PodCastle for correcting speech recognition errors.

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