T alrómur: A large I celandic TTS corpus (2021)

Abstract: We present Talrómur, a large high-quality Text-To-Speech (TTS) corpus for the Icelandic language. This multi-speaker corpus contains recordings from 4 male speakers and 4 female speakers of a wide range in age and speaking style. The corpus consists of 122,417 single utterance recordings equating to approximately 213 hours of voice data. All speakers read from the same script which has a high coverage of possible Icelandic diphones. Manual analysis of 15,956 utterances indicates that the corpus has a reading mistake rate no higher than 0.25%. We additionally present results from subjective evaluations of the different voices with regards to intelligibility, likeability and trustworthiness.

## introduction all statistical tts models require some training data to learn the mapping from text to speech. unit selection tts models are capable of producing an intelligible voice using less than 2 hours of aligned speech @xcite . hmm-based tts models can produce somewhat natural-sounding speech using less than 500 utterances @xcite . the more recent neural end-toend models have reached a considerably higher mean opinion score (mos) in regard to naturalness. however, they require a much larger training corpus; most require tens of thousands of utterances to converge and reach natural sounding synthesis @xcite . the widely used lj speech corpus consists of 13,100 recordings amounting to approximately 24 hours @xcite . to produce high quality synthesised speech with minimal noise, the corpora used for training tts models are most often captured in a studio under supervision. new approaches have lowered the language-specific expertise needed for high quality tts but at the cost of requiring larger amounts of training data @xcite @xcite . the large amount of data needed and the quality of that data limits the ability of many low resource language communities to benefit from these recent advancements in the tts domain. the icelandic language program (ilp) is a 5 year government funded program to make the icelandic language viable in the digital world @xcite . tts development for icelandic is a significant part of the ilp ranging from unit selection voices to multi-speaker tts models. a prerequisite for all tts projects of the ilp is a large high quality tts corpus which up to this point has not been available for open use @xcite . previous work in spoken language technology for icelandic has been more focused on speech recognition, both in terms of data acquisition and acoustic modelling @xcite ) @xcite @xcite . since most of that data is found or crowd-sourced data from multiple speakers it is not ideal for speech synthesis where low background noise and high recording quality is important. an icelandic pronunciation dictionary for tts exists as well as a limited text normalisation system @xcite . to address the lack of high quality icelandic tts data, talrómur has been created. ## the talrómur corpus one of the aims of the talrómur project is to attain diversity in age, speaking style, dialect and prosody. voice samples from speaker applicants were analysed and evaluated with this and a subjective evaluation of pleasantness in mind. each participating speaker got a recording schedule, typically two hours each working day until completion. dialect diversity is low in iceland and six main but rather similar regional variants are listed in the icelandic pronunciation dictionary @xcite . speakers a-f all speak in the most frequent standard dialect while speakers g and h speak in the second most frequent regional variant. speakers a, b and c differ a bit from the rest of the group and their qualities deserve a specific mention. speaker a was the first speaker we recorded. at that time the development of the recording client was ongoing and we had limited experience with the studio and equipment. as shown in speaker b is a 70 year old man with limited eyesight. this speaker often had issues with reading the prompts fluently. this results in unnatural pauses in the middle of sentences that correspond with where the line is split on the screen. we have looked into using silence detection to remove these silences and current results suggest that this task is easily automated. we release the data in the raw format however, without any trimming. speaker c is a female voice actor with a deep, breathy voice. this speaker's recordings are more similar to audio-book recordings in that they have a more animated speaking style when compared to the other speakers. technical details each speaker reads single sentence prompts from the same reading list. the reading list was de-figure 1 : mel-frequency spectrograms of all speakers saying the same phrase: "ég, ég er sko, ég er ekki sko, alveg viss um þetta". recording sessions were carried out in a stu-dio at the national broadcaster of iceland. after recording the first 2 speakers, the project was moved to a different studio at the national broadcaster due to restrictions caused by the covid-19 pandemic. the last two speakers reside in northern iceland and they were therefore recorded in a third studio. the recordings were captured between november 2019 and september 2020. since the speakers read prompts from the same reading list nearly all sentences in the corpus are spoken by multiple speakers. this makes the corpus ideal for multi-speaker tts development, prosody transfer, voice conversion and other research domains where the speaker identity and linguistic content have to be disentangled by the tts model @xcite . the same recording hardware was used for all recordings. the hardware consisted of an akg uls series condenser microphone equipped with a ck-61 cardioid capsule, an spl channel one pre-amplifier and a clarett 2pre sound card. the recordings are captured using a recording client specifically made for this project @xcite . we store some information about every recording captured, such as how the text appeared on the monitor to the speaker, the session id and technical information about the recordings. most recordings are sampled at 48khz with a 16 bit depth. some recordings of speakers a and b are sampled at 44.1khz. all recordings are single channel. ## recording analysis we have analysed a portion of the recordings for quality. of the approximately 122,417 recordings 15,956 recordings have been analysed. using a proprietary tool, human evaluators are asked to first listen to a single recording and then indicate whether the recording matches the prompt and whether the recording quality is good. we specifically ask the evaluators to indicate whether the volume is either too high, resulting in pops or distortions, or too low making the recording hard to comprehend or whether any other audio flaws are audible in the recording. of the recordings analysed 613 were marked as bad or about 3.8%. only 1.23% of the recordings were indicated to have a mismatch between the prompt and the recording. upon further inspection it seems that the evaluators marked recordings with untimely silences as prompt mismatches. most of those are spoken by speaker b as explained in section 2. after a second pass over the evaluations we are confident that a better estimate of prompt mismatches is no more than 0.25%. the rate of audio flaws is 2.17% but reviewing the samples in question revealed that a significant portion of these recordings do not have any unwanted artefacts. upon inspection we believe some of these recordings have a higher than normal volume, making them sound unpleasant when compared to other recordings. this is particularly common for speaker b. the volume of recordings can be too high if the speaker has moved too close to the microphone, the hardware has not been configured correctly or the speaker speaks with more effort than is natural to the speaker. there are however some recordings that do have unwanted artefacts. in most cases this consists of a small pop at a random location in the recording. these pops mostly appear in recordings from speakers a and b and we therefore deduce that the source of this artefact is the hardware configuration in the recording studio for those speakers. ## subjective listening experiment to gain further information about which voice would be most suitable for general tts use, we set up a subjective listening experiment with 50 participants. during the listening experiment, the participants listen to a single recording at a time. they are then asked one of three questions foot_0 : q1: how easy is it to understand this voice? q2: how pleasant is this voice? q3: how trustworthy is this voice? 1 . prosodypro was used for pitch tracking @xcite . the participants then rate the recording on a scale from 1 to 5, e.g. from very untrustworthy to very trustworthy. before starting the evaluation participants are made aware that the sentences being spoken should not affect their judgement and that they should focus on the voice itself. each participant listens to 3 recordings from each speaker for each of the three questions, resulting in 24 evaluations per question and 72 evaluations in total per participant. we used a balanced latin square experimental design with 24 different recordings tested for each evaluation question @xcite . this resulted in 1074 q1 responses, 1074 q2 responses and 1088 q3 responses. the number of responses per utterance ranges from 4 to 8. results from this experiment are shown in ## summary and future work in this paper we introduce the talrómur corpus which is the result of the first tts data acquisition phase of the icelandic language program. talrómur is a large, high quality speech corpus designed specifically for tts. the corpus consists of 8 different voices with a wide range in prosodic effect, speaking style and age. the quality and amount of