prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Is Arabic one of the 11 languages in CoVost?
Context 1: Introduction
End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems  The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems  Several corpora have been developed in recent years  post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations  di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only  beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks  godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort  woldeyohannis provided an Amharic-English ST corpus in the tourism domain  boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7  Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs  This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9  Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings  The corpus we introduce has larger speech durations and more translation tokens  It is diversified with multiple speakers per transcript/translation  Finally, we provide additional out-of-domain test sets 
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora  We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9 3 hours of speech  Both corpora are created at the sentence level and do not require additional alignments or segmentation  Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models  CoVoST is released under CC0 license and free to use  The Tatoeba evaluation samples are also available under friendly CC licenses  All the data can be acquired at https://github com/facebookresearch/covost 
Data Collection and Processing ::: Common Voice (CoVo)
Common Voice BIBREF10 is a crowdsourcing speech recognition corpus with an open CC0 license  Contributors record voice clips by reading from a bank of donated sentences  Each voice clip was validated by at least two other users  Most of the sentences are covered by multiple speakers, with potentially different genders, age groups or accents 
Raw CoVo data contains samples that passed validation as well as those that did not  To build CoVoST, we only use the former one and reuse the official train-development-test partition of the validated data  As of January 2020, the latest CoVo 2019-06-12 release includes 29 languages  CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese 
Validated transcripts were sent to professional translators  Note that the translators had access to the transcripts but not the corresponding voice clips since clips would not carry additional information  Since transcripts were duplicated due to multiple speakers, we deduplicated the transcripts before sending them to translators  As a result, different voice clips of the same content (transcript) will have identical translations in CoVoST for train, development and test splits 
In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11  1) For German-English, French-English and Russian-English translations, we computed sentence-level BLEU BIBREF12 with the NLTK BIBREF13 implementation between the human translations and the automatic translations produced by a state-of-the-art system BIBREF14 (the French-English system was a Transformer big BIBREF15 separately trained on WMT14)  We applied this method to these three language pairs only as we are confident about the quality of the corresponding systems  Translations with a score that was too low were manually inspected and sent back to the translators when needed  2) We manually inspected examples where the source transcript was identical to the translation  3) We measured the perplexity of the translations using a language model trained on a large amount of clean monolingual data BIBREF14  We manually inspected examples where the translation had a high perplexity and sent them back to translators accordingly  4) We computed the ratio of English characters in the translations  We manually inspected examples with a low ratio and sent them back to translators accordingly  5) Finally, we used VizSeq BIBREF16 to calculate similarity scores between transcripts and translations based on LASER cross-lingual sentence embeddings BIBREF17  Samples with low scores were manually inspected and sent back for translation when needed 
We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint 
Data Collection and Processing ::: Tatoeba (TT)
Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available  Its sentences are on average shorter than those in CoVoST (see also Table TABREF2) given the original purpose of language learning  Sentences in TT are licensed under CC BY 2 0 FR and part of the speeches are available under various CC licenses 
We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVoST development and test sets  We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed  We further filter these samples by sentence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences  This makes the resulting evaluation set closer to real-world scenarios and more challenging 
We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria  Finally, we report the overlap between CoVo transcripts and TT sentences in Table TABREF5  We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST 
Data Analysis ::: Basic Statistics
Basic statistics for CoVoST and TT are listed in Table TABREF2 including (unique) sentence counts, speech durations, speaker demographics (partially available) as well as vocabulary and token statistics (based on Moses-tokenized sentences by sacreMoses) on both transcripts and translations  We see that CoVoST has over 327 hours of German speeches and over 171 hours of French speeches, which, to our knowledge, corresponds to the largest corpus among existing public ST corpora (the second largest is 110 hours BIBREF18 for German and 38 hours BIBREF19 for French)  Moreover, CoVoST has a total of 18 hours of Dutch speeches, to our knowledge, contributing the first public Dutch ST resource  CoVoST also has around 27-hour Russian speeches, 37-hour Italian speeches and 67-hour Persian speeches, which is 1 8 times, 2 5 times and 13 3 times of the previous largest public one BIBREF7  Most of the sentences (transcripts) in CoVoST are covered by multiple speakers with potentially different accents, resulting in a rich diversity in the speeches  For example, there are over 1,000 speakers and over 10 accents in the French and German development / test sets  This enables good coverage of speech variations in both model training and evaluation 
Data Analysis ::: Speaker Diversity
As we can see from Table TABREF2, CoVoST is diversified with a rich set of speakers and accents  We further inspect the speaker demographics in terms of sample distributions with respect to speaker counts, accent counts and age groups, which is shown in Figure FIGREF6, FIGREF7 and FIGREF8  We observe that for 8 of the 11 languages, at least 60% of the sentences (transcripts) are covered by multiple speakers  Over 80% of the French sentences have at least 3 speakers  And for German sentences, even over 90% of them have at least 5 speakers  Similarly, we see that a large portion of sentences are spoken in multiple accents for French, German, Dutch and Spanish  Speakers of each language also spread widely across different age groups (below 20, 20s, 30s, 40s, 50s, 60s and 70s) 
Baseline Results
We provide baselines using the official train-development-test split on the following tasks: automatic speech recognition (ASR), machine translation (MT) and speech translation (ST) 
Baseline Results ::: Experimental Settings ::: Data Preprocessing
We convert raw MP3 audio files from CoVo and TT into mono-channel waveforms, and downsample them to 16,000 Hz  For transcripts and translations, we normalize the punctuation, we tokenize the text with sacreMoses and lowercase it  For transcripts, we further remove all punctuation markers except for apostrophes  We use character vocabularies on all the tasks, with 100% coverage of all the characters  Preliminary experimentation showed that character vocabularies provided more stable training than BPE  For MT, the vocabulary is created jointly on both transcripts and translations  We extract 80-channel log-mel filterbank features, computed with a 25ms window size and 10ms window shift using torchaudio  The features are normalized to 0 mean and 1 0 standard deviation  We remove samples having more than 3,000 frames or more than 256 characters for GPU memory efficiency (less than 25 samples are removed for all languages) 
Baseline Results ::: Experimental Settings ::: Model Training
Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing  For MT, we use a Transformer base architecture BIBREF15, but with 3 encoder layers, 3 decoder layers and 0 3 dropout  We use a batch size of 10,000 frames for ASR and ST, and a batch size of 4,000 tokens for MT  We train all models using Fairseq BIBREF20 for up to 200,000 updates  We use SpecAugment BIBREF21 for ASR and ST to alleviate overfitting 
Baseline Results ::: Experimental Settings ::: Inference and Evaluation
We use a beam size of 5 for all models  We use the best checkpoint by validation loss for MT, and average the last 5 checkpoints for ASR and ST  For MT and ST, we report case-insensitive tokenized BLEU BIBREF22 using sacreBLEU BIBREF23  For ASR, we report word error rate (WER) and character error rate (CER) using VizSeq 
Baseline Results ::: Automatic Speech Recognition (ASR)
For simplicity, we use the same model architecture for ASR and ST, although we do not leverage ASR models to pretrain ST model encoders later  Table TABREF18 shows the word error rate (WER) and character error rate (CER) for ASR models  We see that French and German perform the best given they are the two highest resource languages in CoVoST  The other languages are relatively low resource (especially Turkish and Swedish) and the ASR models are having difficulties to learn from this data 
Baseline Results ::: Machine Translation (MT)
MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation)  For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch  Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model  Table TABREF20 shows the BLEU scores of MT models  We notice that the results are consistent with what we see from ASR models  For example thanks to abundant training data, French has a decent BLEU score of 29 8/25 4  German doesn't perform well, because of less richness of content (transcripts)  The other languages are low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques 
Baseline Results ::: Speech Translation (ST)
CoVoST is a many-to-one multilingual ST corpus  While end-to-end one-to-many and many-to-many multilingual ST models have been explored very recently BIBREF8, BIBREF9, many-to-one multilingual models, to our knowledge, have not  We hence use CoVoST to examine this setting  Table TABREF22 and TABREF23 show the BLEU scores for both bilingual and multilingual end-to-end ST models trained on CoVoST  We observe that combining speeches from multiple languages is consistently bringing gains to low-resource languages (all besides French and German)  This includes combinations of distant languages, such as Ru+Fr, Tr+Fr and Zh+Fr  Moreover, some combinations do bring gains to high-resource language (French) as well: Es+Fr, Tr+Fr and Mn+Fr  We simply provide the most basic many-to-one multilingual baselines here, and leave the full exploration of the best configurations to future work  Finally, we note that for some language pairs, absolute BLEU numbers are relatively low as we restrict model training to the supervised data  We encourage the community to improve upon those baselines, for example by leveraging semi-supervised training 
Baseline Results ::: Multi-Speaker Evaluation
In CoVoST, large portion of transcripts are covered by multiple speakers with different genders, accents and age groups  Besides the standard corpus-level BLEU scores, we also want to evaluate model output variance on the same content (transcript) but different speakers  We hence propose to group samples (and their sentence BLEU scores) by transcript, and then calculate average per-group mean and average coefficient of variation defined as follows:
and
where $G$ is the set of sentence BLEU scores grouped by transcript and $G^{\prime } = \lbrace g | g\in G, |g|>1, \textrm {Mean}(g) > 0 \rbrace $ 
$\textrm {BLEU}_{MS}$ provides a normalized quality score as oppose to corpus-level BLEU or unnormalized average of sentence BLEU  And $\textrm {CoefVar}_{MS}$ is a standardized measure of model stability against different speakers (the lower the better)  Table TABREF24 shows the $\textrm {BLEU}_{MS}$ and $\textrm {CoefVar}_{MS}$ of our ST models on CoVoST test set  We see that German and Persian have the worst $\textrm {CoefVar}_{MS}$ (least stable) given their rich speaker diversity in the test set and relatively small train set (see also Figure FIGREF6 and Table TABREF2)  Dutch also has poor $\textrm {CoefVar}_{MS}$ because of the lack of training data  Multilingual models are consistantly more stable on low-resource languages  Ru+Fr, Tr+Fr, Fa+Fr and Zh+Fr even have better $\textrm {CoefVar}_{MS}$ than all individual languages 
Conclusion
We introduce a multilingual speech-to-text translation corpus, CoVoST, for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  We also provide baseline results, including, to our knowledge, the first end-to-end many-to-one multilingual model for spoken language translation  CoVoST is free to use with a CC0 license, and the additional Tatoeba evaluation samples are also CC-licensed 
Context 3: Introduction
From a group of small users at the time of its inception in 2009, Quora has evolved in the last few years into one of the largest community driven Q&A sites with diverse user communities  With the help of efficient content moderation/review policies and active in-house review team, efficient Quora bots, this site has emerged into one of the largest and reliable sources of Q&A on the Internet  On Quora, users can post questions, follow questions, share questions, tag them with relevant topics, follow topics, follow users apart from answering, commenting, upvoting/downvoting etc  The integrated social structure at the backbone of it and the topical organization of its rich content have made Quora unique with respect to other Q&A sites like Stack Overflow, Yahoo! Answers etc  and these are some of the prime reasons behind its popularity in recent times  Quality question posting and getting them answered are the key objectives of any Q&A site  In this study we focus on the answerability of questions on Quora, i e , whether a posted question shall eventually get answered  In Quora, the questions with no answers are referred to as “open questions”  These open questions need to be studied separately to understand the reason behind their not being answered or to be precise, are there any characteristic differences between `open' questions and the answered ones  For example, the question “What are the most promising advances in the treatment of traumatic brain injuries?” was posted on Quora on 23rd June, 2011 and got its first answer after almost 2 years on 22nd April, 2013  The reason that this question remained open so long might be the hardness of answering it and the lack of visibility and experts in the domain  Therefore, it is important to identify the open questions and take measures based on the types - poor quality questions can be removed from Quora and the good quality questions can be promoted so that they get more visibility and are eventually routed to topical experts for better answers 
Characterization of the questions based on question quality requires expert human interventions often judging if a question would remain open based on factors like if it is subjective, controversial, open-ended, vague/imprecise, ill-formed, off-topic, ambiguous, uninteresting etc  Collecting judgment data for thousands of question posts is a very expensive process  Therefore, such an experiment can be done only for a small set of questions and it would be practically impossible to scale it up for the entire collection of posts on the Q&A site  In this work, we show that appropriate quantification of various linguistic activities can naturally correspond to many of the judgment factors mentioned above (see table 2 for a collection of examples)  These quantities encoding such linguistic activities can be easily measured for each question post and thus helps us to have an alternative mechanism to characterize the answerability on the Q&A site 
There are several research works done in Q&A focusing on content of posts  BIBREF0 exploit community feedback to identify high quality content on Yahoo! Answers  BIBREF1 use textual features to predict answer quality on Yahoo! Answers  BIBREF2 , investigate predictors of answer quality through a comparative, controlled field study of user responses  BIBREF3 study the problem of how long questions remain unanswered  BIBREF4 propose a prediction model on how many answers a question shall receive

ground_truth: No

No

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. what accents are present in the corpus?
Context 2: Introduction
Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks  Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets  In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0 
In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1  Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases  The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3  The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64)  Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task 
Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems  Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects  While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian  To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian 
The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods  The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017  The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019  In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics  In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part  Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups  Additional baseline results are also reported for Persian speech recognition  However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions  They will be defined and reported in the database technical documentation and in a future journal paper 
Data Collection
DeepMine is publicly available for everybody with a variety of licenses for different users  It was collected using crowdsourcing BIBREF4  The data collection was done using an Android application  Each respondent installed the application on his/her personal device and recorded several phrases in different sessions  The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase  For more information about data collection scenario, please refer to BIBREF4 
Data Collection ::: Post-Processing
In order to clean-up the database, the main post-processing step was to filter out problematic utterances  Possible problems include speaker word insertions (e g  repeating some part of a phrase), deletions, substitutions, and involuntary disfluencies  To detect these, we implemented an alignment stage, similar to the second alignment stage in the LibriSpeech project BIBREF5  In this method, a custom decoding graph was generated for each phrase  The decoding graph allows for word skipping and word insertion in the phrase 
For text-dependent and text-prompted parts of the database, such errors are not allowed  Hence, any utterances with errors were removed from the enrollment and test lists  For the speech recognition part, a sub-part of the utterance which is correctly aligned to the corresponding transcription is kept  After the cleaning step, around 190 thousand utterances with full transcription and 10 thousand with sub-part alignment have remained in the database 
Data Collection ::: Statistics
After processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female  297 of the respondents could not read English and have therefore read only the Persian prompts  About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i e  women are over-represented in terms of sessions, even though their number is 17% smaller than that of males  Other useful statistics related to the database are shown in Table TABREF4 
The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples 
DeepMine Database Parts
The DeepMine database consists of three parts  The first one contains fixed common phrases to perform text-dependent speaker verification  The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots)  This part can also serve for Persian ASR training  Each part is described in more details below  Table TABREF11 shows the number of unique phrases in each part of the database  For the English text-dependent part, the following phrases were selected from part1 of the RedDots database, hence the RedDots can be used as an additional training set for this part:
“My voice is my password ”
“OK Google ”
“Artificial intelligence is for real ”
“Actions speak louder than words ”
“There is no such thing as a free lunch ”
DeepMine Database Parts ::: Part1 - Text-dependent (TD)
This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode  Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded 
We have created three experimental setups with different numbers of speakers in the evaluation set  For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data)  The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set  Note that, for English, we have filtered the (Persian native) speakers by the ability to read English  Therefore, there are fewer speakers in each set for English than for Persian  There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set 
For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data  All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance  All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session  From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition  In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV)  Note that for Imposter-Wrong (IW) trials (i e  imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case  So, the number of trials for IC and IW cases are the same 
DeepMine Database Parts ::: Part2 - Text-prompted (TP)
For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment  The second mode contains a sequence of 3 month names that will be used as a test utterance  In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words  For a respondent who can read English, 3 random sequences of English digits are also recorded in each session  In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits 
Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16)  However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i e  3 to 9 utterances)  Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i e  same words as in enrollment but in different order)  From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case  In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV  Again, we just create one IW trial for each IC trial 
DeepMine Database Parts ::: Part3 - Text-independent (TI)
In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent  These phrases are chosen mostly from news and Persian Wikipedia  If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session 
This part can be useful at least for three potential applications  First, it can be used for text-independent speaker verification  The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words)  Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section) 
Based on the recording sessions, we created two experimental setups for speaker verification  In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data)  In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set  Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case 
For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test  The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions  The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case  In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances)  From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case  Table TABREF19 shows number of trials for these setups 
For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used 
DeepMine Database Parts ::: Part3 - Speech Recognition
As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition  There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7  Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian  Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training 
We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers  For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set  The remaining utterances of the other speakers are included in the training set  The test-set, large-test-set and train-set contain 5 9, 28 5 and 450 hours of speech respectively 
There are about 8300 utterances in Part3 which contain only Persian full names (i e  first and family name pairs)  Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P)  These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model 
Experiments and Results
Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition 
Experiments and Results ::: Speaker Verification Experiments
We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in BIBREF9, BIBREF10 and applied it to the Persian portion of Part1  In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16 kHz signals using HTK BIBREF11 with 25 ms Hamming windowed frames with 15 ms overlap 
The reported results are obtained with a 400-dimensional gender independent i-vector based system  The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN) BIBREF10  Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores  For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used BIBREF10  We only model the phonemes which appear in the 5 Persian text-dependent phrases 
For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm {NDCF_{0 01}^{min}}$) and NIST SRE10 ($\mathrm {NDCF_{0 001}^{min}}$)  As shown in Table TABREF22, in text-dependent SV there are 4 types of trials: Target-Correct and Imposter-Correct refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, Target-Wrong and Imposter-Wrong refer to trials when speakers uttered a wrong pass-phrase  In this paper, only the correct trials (i e  Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV BIBREF8, BIBREF12 
Table TABREF23 shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup  For filtering trials, the respondents' mobile brand and model were used in this experiment  In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i e  right side of the colon) relate for non-target trials  For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker  The second Y letter means both recordings were done using exactly the same device model  Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i e  manufacturer)  The second Y means that, in addition to the same brand, both devices have the same model  So, the most difficult target trials are “NN”, where the speaker has used different a device at the test time  In the same manner, the most difficult non-target trials which should be rejected by the system are “YY” where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device)  Hence, the similarity in the recording channel makes rejection more difficult 
The first row in Table TABREF23 shows the results for all trials  By comparing the results with the best published results on RSR2015 and RedDots BIBREF10, BIBREF8, BIBREF12, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases  For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0 3% for both genders (Table VI in BIBREF10)  The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i e  without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1 5% (Table XI in BIBREF10)  It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2 1 and 3 7% for males and females respectively 
One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device  This is allows us to analyse the effects of channel compensation methods  The second row in Table TABREF23 corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models  It is clear that severe degradation was caused by this kind of channel effects (i e  decreasing within-speaker similarities while increasing between-speaker similarities), especially for females 
The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment  Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker 
The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system  So, in this case, there is no device mismatch in all trials  By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device 
The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model  Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties 
The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials  For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case 
Looking at the first two and the last row of Table TABREF23, one can notice the significantly worse performance obtained for the female trials as compared to males  Note that these three rows include target trials where the devices used for enrollment do not necessarily match the devices used for recording test utterances  On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable  This suggest that the degraded results for females are caused by some problematic trials with device mismatch  The exact reason for this degradation is so far unclear and needs a further investigation 
In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand  In this case, as was expected, the performance degradation is smaller than in the second row 
Experiments and Results ::: Speech Recognition Experiments
In addition to speaker verification, we present several speech recognition experiments on Part3  The experiments were performed with the Kaldi toolkit BIBREF13  For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used  The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes)  The network is shown in Table TABREF25: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256  The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization BIBREF14  Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words 
Table TABREF26 shows the results in terms of word error rate (WER) for different evaluated methods  As can be seen, the created database can be used to train well performing and practically usable Persian ASR models 
Conclusions
In this paper, we have described the final version of a large speech corpus, the DeepMine database  It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English  In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions  Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition 
We provided several evaluation protocols for each part of the database  The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance  We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method 
As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part  Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance  Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases 
Acknowledgments
The data collection project was mainly supported by Sharif DeepMine company  The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No  19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602" 
Context 3:  BIBREF5 analyze and predict unanswered questions on Yahoo Answers  BIBREF6 study question quality in Yahoo! Answers 
Dataset description
We obtained our Quora dataset BIBREF7 through web-based crawls between June 2014 to August 2014  This crawling exercise has resulted in the accumulation of a massive Q&A dataset spanning over a period of over four years starting from January 2010 to May 2014  We initiated crawling with 100 questions randomly selected from different topics so that different genre of questions can be covered  The crawling of the questions follow a BFS pattern through the related question links  We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions  For each question, we separately crawl their revision logs that contain different types of edit information for the question and the activity log of the question asker 
Linguistic activities on Quora
In this section, we identify various linguistic activities on Quora and propose quantifications of the language usage patterns in this Q&A site  In particular, we show that there exists significant differences in the linguistic structure of the open and the answered questions  Note that most of the measures that we define are simple, intuitive and can be easily obtained automatically from the data (without manual intervention)  Therefore the framework is practical, inexpensive and highly scalable 
Content of a question text is important to attract people and make them engage more toward it  The linguistic structure (i e , the usage of POS tags, the use of Out-of-Vocabulary words, character usage etc ) one adopts are key factors for answerability of questions  We shall discuss the linguistic structure that often represents the writing style of a question asker 
In fig 1 (a), we observe that askers of open questions generally use more no  of words compared to answered questions  To understand the nature of words (standard English words or chat-like words frequently used in social media) used in the text, we compare the words with GNU Aspell dictionary to see whether they are present in the dictionary or not  We observe that both open questions and answered questions follow similar distribution (see fig 1 (b))  Part-of-Speech (POS) tags are indicators of grammatical aspects of texts  To observe how the Part-of-Speech tags are distributed in the question texts, we define a diversity metric  We use the standard CMU POS tagger BIBREF8 for identifying the POS tags of the constituent words in the question

ground_truth: Unanswerable

Unanswerable

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Do the answered questions measure for the usefulness of the answer?
Context 3:  We define the POS tag diversity (POSDiv) of a question $q_i$ as follows: $POSDiv(q_i) = -\sum _{j \in pos_{set}}p_j\times \log (p_j)$ where $p_j$ is the probability of the $j^{th}$ POS in the set of POS tags  Fig 1 (c) shows that the answered questions have lower POS tag diversity compared to open questions  Question texts undergo several edits so that their readability and the engagement toward them are enhanced  It is interesting to identify how far such edits can make the question different from the original version of it  To capture this phenomena, we have adopted ROUGE-LCS recall BIBREF9 from the domain of text summarization  Higher the recall value, lesser are the changes in the question text  From fig 1 (d), we observe that open questions tend to have higher recall compared to the answered ones which suggests that they have not gone through much of text editing thus allowing for almost no scope of readability enhancement 
Psycholinguistic analysis:
The way an individual talks or writes, give us clue to his/her linguistic, emotional, and cognitive states  A question asker's linguistic, emotional, cognitive states are also revealed through the language he/she uses in the question text  In order to capture such psycholinguistic aspects of the asker, we use Linguistic Inquiry and Word Count (LIWC) BIBREF10 that analyzes various emotional, cognitive, and structural components present in individuals' written texts  LIWC takes a text document as input and outputs a score for the input for each of the LIWC categories such as linguistic (part-of-speech of the words, function words etc ) and psychological categories (social, anger, positive emotion, negative emotion, sadness etc ) based on the writing style and psychometric properties of the document  In table 1 , we perform a comparative analysis of the asker's psycholinguistic state while asking an open question and an answered question 
Askers of open questions use more function words, impersonal pronouns, articles on an average whereas asker of answered questions use more personal pronouns, conjunctions and adverbs to describe their questions  Essentially, open questions lack content words compared to answered questions which, in turn, affects the readability of the question  As far as the psychological aspects are concerned, answered question askers tend to use more social, family, human related words on average compared to an open question asker  The open question askers express more positive emotions whereas the answered question asker tend to express more negative emotions in their texts  Also, answered question askers are more emotionally involved and their questions reveal higher usage of anger, sadness, anxiety related words compared to that of open questions  Open questions, on the other hand, contains more sexual, body, health related words which might be reasons why they do not attract answers 
In table 2 , we show a collection of examples of open questions to illustrate that many of the above quantities based on the linguistic activities described in this section naturally correspond to the factors that human judges consider responsible for a question remaining unanswered  This is one of the prime reasons why these quantities qualify as appropriate indicators of answerability 
Prediction model
In this section, we describe the prediction framework in detail  Our goal is to predict whether a given question after a time period $t$ will be answered or not 
Linguistic styles of the question asker
The content and way of posing a question is important to attract answers  We have observed in the previous section that these linguistic as well as psycholinguistic aspects of the question asker are discriminatory factors  For the prediction, we use the following features:

ground_truth: No