Automatic computation of speech rate has several applications in speech technologies, such as automatic evaluation of prosody. Several studies have explored, for example, its use for automatic evaluation of speech fluency (Cucchiarini, Strik, & Boves, 1998, 2000a, 2000b, 2002; Neumeyer, Franco, Digalakis, & Weintraub, 2000; Zechner, Higgins, Xia, & Williamson, 2009; Honig, Batliner, Weilhammer, & Nöth, 2010, among others). Usual approaches to speech rate computation use a phonetic aligner to obtain the necessary phonetic segmentation. This is so because the performance of speech recognition systems, if available, is not good enough to guarantee that the obtained phonetic segmentation is reliable.

Phonetic aligners appear then as an alternative to obtain a more accurate segmentation of the speech chain, but they need the orthographic transcription of the input discourse to be known. If the computation of the speech rate of unrestricted text -not previously known by the system- is attempted, there are some alternatives that do not require a full phonetic segmentation of the input speech to be available, such as the automatic detection of syllabic nuclei. With the aim of exploring this alternative, the current paper compares the performance of two different methods for the automatic computation of the number of syllabic nuclei using a similar technique based on intensity peak detection. The first one is a Praat script described in de Jong and Wempe (2009) and the second one is another Praat script developed by the same authors and other collaborators (de Jong, Pacilly, & Wempe, 2021), in which a different approach to detect intensity peaks is applied. The final goal is to determine which of them would perform better in a task of syllable detection oriented to speech rate calculation and to establish if any of these two methods is adequate to be used in an automatic prosody evaluation system for Spanish.

This paper is structured as follows: Section 2 briefly overviews the related work on this topic, Section 3 describes the experimental setup, Section 4 presents the assessment results, and finally, Sections 5 and 6 sketch the discussion and conclusions, respectively.

Most of the studies that deal with automatic computation of speech rate are based on the transcriptions obtained -either manually or automatically- from speech material. They mainly differ in the units used to compute speech rate. Most of them are based on counting the number of syllables within a specific segment of speech, providing the speech rate computation as the number of syllables per second, while some other works also provide other measures. Verhasselt and Martens (1996), for instance, defines speech rate as the number of phones per second and computes them over the sentences of the TIMIT corpus. Pfitzinger (1996) also used the number of phones per second as speech rate measure over a total of 240 sentences spoken by eight different speakers.

The literature on automatic speech rate computation tools without transcriptions, which is the goal of the current paper, is scarce. One of the most relevant works in this respect is Pfau and Ruske (1998), in which speech rate is computed by means of vowel detection, based on loudness in vowel regions, which tends to be higher than in consonant regions. Similarly, the method of Pellegrino, Farinas and Rouas (2004) is based on an unsupervised vowel detection algorithm scalable to any language. Validation was assessed on a spontaneous speech subset of the OGI Multilingual Telephone Speech Corpus. In Narayanan and Wang (2005) and Wang and Narayanan (2007), the authors present novel methods for speech rate estimation, measured as the number of syllables per second, analyzing the segments contained between pauses in the Switchboard database (Godfrey & Holliman, 1993). Both methods are based on an extension of signal correlation -essential for syllable detection- by including temporal correlation and prominent spectral sub-bands.

The work described in Dekens, Demol, Verhelst and Verhoeve (2007) is also based on the number of syllables per second, and the authors evaluate the performance of several speech estimators on a multilingual database covering Dutch, English, French, Romanian and Spanish, by using sub-band and time correlation to detect the number of vowels and diphthongs.

However, giving that speech rate can be computed using syllables or phones and total time of speech, any tool that identifies either syllable boundaries or vowels can be used for this task, for example, tools that syllabify conversational speech (Landsiedel et al., 2011; Mary et al., 2018) or tools that locate syllable nuclei (Sabu, Chaudhuri, Rao, & Patil, 2021). Using this last method, de Jong et al. (2007) and de Jong & Wempe (2009) compute speech rate over two corpora of spoken Dutch, by identifying peaks in intensity that are preceded by dips, which is then considered as a syllable nucleus. In Sabu et al. (2021), the authors use the TIMIT dataset (Garofolo et al., 1993) and a children’s oral reading corpus created ad hoc, for which they identify vowel sonority by means of local peak picking on a frequency-weighted energy contour.

The performance analysis (see 4.1) shows that both tools are reliable finding syllable nuclei (precision> 0.8 and recall > 0.5, in all cases). Also, both tools perform better with read speech than with spontaneous speech. However, they share a common problem in classification tasks: an imbalanced classification, with more false negatives than false positives, which complicates the assessment. For our data, this is a foreseeable result given that finding a syllable nucleus that is not preceded or followed by an intensity dip is more usual in speech than it is for a voiced intensity peak to be a consonant. This means that, if we want the tool to correctly disregard peaks that are not vowels, we need to use a more restrictive system -which v3 does by requiring a preceding and following intensity dip in order to consider an interval as a syllable nucleus- and that will give us a better result in accuracy and precision, which is exactly what is shown in Table 2, given that precision is as a measure of quality, meaning that the vowels that are marked as vowels with v3 are more likely to be real vowels. However, if we consider the global result of correctly identified and disregarded syllable nuclei (the quantity) a less restrictive rule (i.e., v1, which only considers previous intensity dips) has a better performance as illustrated by Table 2 recall and F-score.

For the aim of this paper, which is the automatic computation of speech rate, quantity measures can prove more relevant than quality measures given that, when computing speech rate, we are not interested in knowing whether the segment is a syllable nucleus but rather in getting a number of syllable nuclei as close as possible to the actual one. That is, if a false positive is later compensated by a missed nucleus the system is still accurate. This is the exact scenario when in a real syllable the automatic tool places the syllable nucleus within the onset instead of in the actual nucleus, but then does not label the vowel as nucleus.

In Table 3, we can clearly see that the number of syllables counted by v1 is closer to the actual number of syllables counted by v3. In other words, v3 is missing a larger number of syllables, which results in larger values of RMSE for v3, both in the read and the spontaneous modalities (Table 4). These results are consistent with those shown in Table 1, also illustrated in Figure 2: the number of detected (true positives) and false positive syllables is greater in v1. The number of missed syllables (false negatives) also contributes to enlarge the underestimation in the syllable counting.

However, the NRMSE metric (Table 5) shows otherwise: the RMSE normalized values by the mean of the measured data in v3 outperform those obtained in v1. The fact is that, although v3 fails more in detecting syllables than v1, such failure is more stable. This is strengthened by the measurement of other metrics such as the standard error (standard deviation of the mean error) and the coefficient of variation -or relative standard deviation- defined as (standard deviation)/mean). For both measurements, v3 shows a better performance than v1 for both read and spontaneous modalities.

On the one hand, this shows that, although v3 fails largely in missing syllables, such failure could be better compensated by a correction factor. On the other hand, and since v3 appears to be more restrictive in the detection conditions of syllables - we need an intensity dip in both side of the vowel and not only one as in v1-, but we can also ensure that the detected syllables come more often from actual syllable nuclei in v3 than in v1, in which the detected syllable could come more often from false nuclei. This is also strengthened by the larger number of true negatives in v3 encountered in Table 1 for both modalities.

The results presented and discussed in the previous sections indicate, on the one hand, that both methods of syllable detection are not fully reliable yet to face a speech rate analysis task: both detect a number of syllables which is remarkably lower than the number of syllables obtained from a manual annotation. However, v1 seems to offer a better performance for this task than v3, as the number of detected syllables is closer to the manually obtained value, which compensates the fact that it is less precise in the detection of actual syllables, a fact that is secondary in a speech rate calculation task if the number of detected syllables is close enough to the number of manually annotated ones.

On the other hand, the results also show that, although v3 detects in general less true syllables than v1, it seems more adequate for tasks in which it is important that detected syllables correspond to actual syllables, such as automatic acoustic measurements of corpora involving the detection of syllabic nuclei.