Learning a Translation Model from Word Lattices
Oliver Adams,1 Graham Neubig,2 Trevor Cohn,1 Steven Bird1
The University of Melbourne, Australia
Nara Institute of Science and Technology, Japan1

2

oadams@student.unimelb.edu.au, neubig@is.naist.jp,
tcohn@unimelb.edu.au, sbird@unimelb.edu.au

Abstract
Translation models have been used to improve automatic
speech recognition when speech input is paired with a written translation, primarily for the task of computer-aided translation. Existing approaches require large amounts of paralleltext for training the translation models, but for many language
pairs this data is not available. We propose a model for learning lexical translation parameters directly from the word lattices
for which a transcription is sought. The model is expressed
through composition of each lattice with a weighted finite-statetransducer representing the translation model, where inferenceis performed by sampling paths through the composed finitestate transducer. We show consistent word error rate reductions in two datasets, using between just 20 minutes and 4 hoursof speech input, additionally outperforming a translation modeltrained on the 1-best path.
Index Terms: speech recognition, machine translation

1. Introduction
The quality of automatic speech recognition (ASR) can often
be improved by harnessing textual translations [1, 2, 3]. One
use case is computer-aided translation (CAT) with interfaces
where a human translator speaks their translation of a written
document. Although monolingual ASR alone may suffice for
ergonomics or efficiency, performance gains have been demonstrated by using translation models (TMs) to help disambiguate
the translator’s speech. Thus, when distinguishing acousticallyambiguous candidates such as ‘recognise speech’ from ‘wrecka nice beach’, translation model scores make it easier to disambiguate these utterances when paired with a written translationin another language.
Existing work requires externally sourced parallel text data,a scarce resource for most language pairs even when each language has substantial monolingual data. We propose a method
for improving speech recognition accuracy by harnessing awritten translation in another language, even when no parallel text corpora are available. This is achieved by training atranslation model directly on word lattice data from utterances
we seek to recognize paired with written translations in anotherlanguage.
We use a generative model that assumes the acoustic signal and written translation are produced by some underlying
word sequence we seek to recover. This model is expressed bycomposing a word lattice that expresses information from the
ASR acoustic and language models with a weighted finite-statetransducer (WFST) that expresses lexical translation probabilities constrained by the observed translation. These parameters

are learnt by sampling paths (word sequences) through the composed WFST, together with their alignments to translations. A
likely source sentence is recovered by finding the shortest pathin the WFST.
In experiments on the Fisher and CALLHOME Spanish–
English Speech Translation Corpus [4], we compare word error
rates with those of ASR 1-best paths and a stronger baseline thattrains an existing translation model on 1-best recognition results. We demonstrate reduced word error rates of 4.1% to 5.6%
relative over the 1-best paths, and also show 2.3% to 2.4% relative improvement over the alternative model that uses parameters learnt using 1-best paths. These results indicate that themere existence of translations of what is to be transcribed canhelp with ASR. Moreover, it shows promise for models of thistype for computer-aided translation and also for speech recognition for low-resource languages, where neither translation nor
recogniser technologies are currently adequate.

2. Related work
There has been extensive work on combining ASR and statistical machine translation (SMT) systems, with the work largely
focused on coupling the systems for the problem of speechtranslation [5, 6, 7, 8]. There has also been a variety of work on
using translation models to improve ASR performance [9, 10],which includes the popular CAT use case [11, 1]. Work has typically involved modifying language model probabilities in the
ASR system [12, 13], and improving decision-making between
the N-best hypotheses of ASR systems [14, 15]. Additionally,
word lattice based approaches have also been pursued [2, 3].
The transcription of multiple streams of interpreted speech has
also been addressed with the aid of machine translation [16, 17].
However, in all of these works the translation models are trainedon substantial external written corpora such as European parliament proceedings or the Canadian Hansards.
There has been scarce work in the area of training translation models from speech data. Notably, [18, 19, 20] investigate using interpreted speech from European Parliament Plenary Sessions, first performing alignment of speech utterances
before subsequent word-level alignment of their 1-best ASR hypotheses with a traditional word aligner.
Our approach differs in that (a) it depends on no prior parallel text training data, and that (b) the translation model is trained
directly from word lattices to harness more information than isavailable in the 1-best ASR hypothesis alone.
This approach uses techniques similar to those found in theBayesian word alignment literature [21, 22, 23], though rather
than sampling alignments between observed source and targetson/0.210
3son:never/10.31

4

son:knows/2.4522

nunca/1.67
5

son:but/7.8122

Figure 1: Part of a lattice for ‘Ahá, pero una nunca sabe’ withtranslation ‘Aha, but one never knows’. Note that ‘son’ is incorrectly given higher probability than ‘nunca’. Probabilities are
expressed as negative log probabilities.

4

son:aha/12.582
son:one/1.9881
3

nunca:but/15.175

f :e/−logP (e|f ) ∀e ∈ eI1

nunca:knows/5.2487
nunca:one/6.6167

start

5

nunca:never/1.7876

Figure 2: Reduced translation model template and hypotheticaltranslation model parameters. Edges are added to the WFST
only if the e is present in written translation.

word sequences, we sample paths through the source word lattice jointly with alignments to the target (translation) word sequences.
This approach is also similar to [24], where a lexicon andlanguage model are learnt directly from phoneme lattices. However, rather than composing a phoneme lattice with a lexicon
and a language model WFST, we compose a word lattice with a
WFST representing a translation model.

nunca:aha/14.038

Figure 3: Part of the lattice composed with the translation model
WFST. Each edge for a given Spanish word is replaced witha set of edges that transduce to different English words withprobabilities re-weighted by the translation model. Note that‘nunca’ is now correctly given more weight than ‘son’, unlike
in Figure 1.
served sequence eI1 of translated words
fˆ1J = argmax P (f1J |xT1 , eI1 )f1J

3. Model description= argmax P (eI1 , xT1 , f1J ).

(2)

f1J

3.1. ASR lattices

Assuming conditional independence of xT1 and eI1 given

ASR is characterized by the search problem
fˆ1J = argmax P (xT1 |f1J )P (f1J )f1J , the problem can be factorized to
(1)

f1J

where f1J represents an unobserved sequence of words f1 . . . fJ

that produced the sequence of observed acoustic features
x1 . . . xT , and fˆ1J is our best guess of those words.
An ASR lattice encodes multiple ASR hypotheses, as
shown in Figure 1 where each edge corresponds to a word fi .
The acoustic model (AM) and language model (LM) probabilities P (xT1 |f1J ) and P (f1J ) are captured by the weights of theedges.
For any path f1J through the lattice,its probability can simQ
ply be determined with P (f1J ) = Jj=1 PL (fj ), where PL (fj )is the probability of the ith edge in that path of the lattice.
The most likely path can be determined by finding the shortest
path through the lattice in question using Dijkstra’s algorithm,
if probabilities are represented as negative log probabilities.
3.2. Proposed model
The proposed model also uses translation models to aid in ASR
by incorporating additional information in the form of an ob-

fˆ1J = argmax P (eI1 |f1J )P (xT1 |f1J )P (f1J )
f1J

(3)

This problem can be reduced to a similar shortest-path
problem with inference of f1J limited to the paths that occur
in the original lattice. This is done by composing our originallattices with a WFST that represents translation model probabilities, as shown in Figure 2. The resulting composition of the
final lattice and the constrained translation model can be seen in
Figure 3.1
In this framework, each path represents a sequence ofsource tokens f1J and the sequence of target words they arealigned to, ea1 . . . eaJ ,2 with probability:
P (eI1 , xT1 , f1J ) ≈ P (ea1 . . . eaJ |f1J )P (xT1 |f1J )P (f1J )
=

J
Y


P (eaj |fj )P (fj ) P (xT1 |f1J )

(4)

j=1
1 For the examples in the figures and this formulation, we disregard

the possibility of the null token, which we discuss in Section 5.
2 In our implementation, we employ an optimisation by consideringalignment only to unique word types on the target side, rather than tokens. This is possible as the probability will be the same for identicaltokens.

In this case the shortest path corresponds to determining
the most likely source f1J and the alignments aJ1 . Since distinctalignment paths may have the same source form f1J , fˆ1J is mostˆJ
accurately foundP by marginalizing over the alignments: f1 =argmaxf J aJ P (f1J , ea1 . . . eaJ ). However, for reasons of
1
1
computational tractability we simply approximate fˆJ with thesource side of the most likely path.

1

We now turn to the task of determining parameters which willallow us to the find the form of f1J as discussed above. We
model these parameters using a Dirichlet distribution:ce,f + αPbase (e|f )P0
e0 ce ,f + α

P (e|f )P (f |e)P (e|f )sume0 P (e0 |f )P (f |e)sumf 0 P (f 0 |e)

0.555
0.552
0.568

Lattice TMα, λ = 1 α, λ = best
0.559
0.556
0.542
0.541
0.574
0.570

0.547

0.539

0.539

Table 1: Parameter variations for tuning on the CALLHOME
training set. ASR 1-best accuracy is 0.569.

4. Parameter learning

P (e|f ; α) =1-best TM(5)

to degenerate alignments where most fi end up aligning to thenull token since it is present in every sentence.
Alternative formulations include use of P (f |e) instead forboth training and testing, and/or the use of normalizations ofthese probabilities. Notably, we propose and test an approach|e)that uses P P0 (fwhere each f 0 is a token occurring in the
P (f 0 |e)fwhere e0 are target tokens in eI1 , ce,f is a count of how manytimes f has aligned to e in the rest of the dataset (holding out
this current instance), Pbase is a uniform prior, and α is theprior’s strength. This requires that we have a set of source tokens and alignments A = {hf1Jn , aJ1 n i|n ∈ N } where Jn isthe number of edges in a sampled path from the nth WFST
composition.
We want to base these parameters on alignments A with aprobability proportional to A’s ability to explain the data:
Z
P (A|X ; α) =P (A|X , T )P (T ; α)dT
(6)T

where X represents all the observed data, including word lattices and written translations, and T represents a translation
model that we assume prior information about and whose parameters we integrate over.
Draws from the above distribution over A are approximatedusing blocked Gibbs sampling, where each block corresponds
to one of the composed WFSTs. The sampling of paths throughthese composed WFSTs can be achieved using the method of
forward-filtering/backward-sampling as described in [24]. This
sampling method first computes forward probabilities in thesame way the forward-backward algorithm for hidden Markov
models does. It then samples paths backwards from the end of
the WFST using these forward probabilities to yield a path withprobability proportional to the total probability of the edges in
the path.
After sampling a path consisting of lexical alignments, thecounts of those lexical alignments are added to the cache used
to calculate the Dirichlet posterior of e given f as per (5) before
the next WFST is constructed and sampled from. With repeatedsampling the alignment set A is drawn approximately from the
distribution in (6). Sampling sets of alignments n times, we use
these alignment sets A1 . . . An to create a set of point estimates
T1 . . . Tn for T . We then average these parameters to create a final expected T for the purposes of decoding using the approachof Section 3.

5. Variations on parameter formulation
In the previous formulation of (4) we discussed using the conditional direction of P (e|f ) as the translation model parameters. A problem with using P (e|f ) is that values of e with ahigher marginal probability P (e) tend to have a higher conditional probability P (e|f ). This problem makes itself especiallyclear when permitting null tokens on the English side, as it leads

original lattice.3 The normalization denominator does not explicitly affect distinguishing between different source words inthe WFST when sampling or decoding. However, it aids inaligning to the correct target word e by biasing towards alignments where f is most likely relative to its peers given e. Improving the alignments this way thus affects the translation
model and, subsequently, the future paths chosen when sampling or decoding.
We also introduce a lattice weight λ during both training
and testing. The contribution of original lattice probabilities
from the acoustic model and language model against the translation model probabilities can be increased by simply multiplying the negative log probabilities by λ.

6. Experimental evaluation
6.1. Experimental setup
For the experiments we used the Fisher and CALLHOME
Spanish–English Speech Translation Corpus [4], which conveniently offers Spanish word lattices and crowdsourced English
translations. For training and testing we use the predefined test
subsets of these corpora which are 213 minutes (39,978 words)
for the Fisher corpus and 106 (18,792 words) for the CALLHOME corpus. The LDC human transcriptions [25, 26] areused as a gold standard against which to evaluate the ASR.
Our preprocessing involved lowercasing all text, and removing
punctuation from both the Spanish and English sides. We additionally removed from the corpus a small number of empty
sentences and empty lattices.
To evaluate how harnessing the English translations can improve use of the Spanish word lattices, we evaluate the word error rate of the chosen path through the composed WFST against
the LDC transcriptions. We compare our approach, which werefer to as Lattice TM, with a similar method where the translation model is instead trained from 1-best paths from the latticeusing GIZA++ [27], which we refer to as 1-best TM.
6.2. Tuning and choice of parameterization
We tuned two hyperparameters on the CALLHOME training set
of approximately 14.5 hours: the lattice weight, λ, and the concentration parameter of the Dirichlet distributions, α. Tuning
involved a simple grid search of λ, α over the values 0.5, 1, 2,
and 4. We found no significant improvements beyond the de3 This denominator does not equal 1 as all f 0 in the lattice constituteonly a subset of the source vocabulary.

CALLHOME test set

0.39
0.38
0.37
0.36
0.35
0.34
0.33

0.62
0.61
0.60
0.59
0.58
0.57
0.56
0

50 100 150 200

0 20 40 60 80 100# minutes of test set used for TM training
ASR 1-best;

1-best TM;

Lattice TM

Figure 4: Word error rates on the test sets when training thetranslation model on different subsets of that test set.
ASR 1-best
1-best TM
Lattice TM

Fisher
0.355
0.343
0.335

Fisher test set

WER

WER

Fisher test set

CALLHOME
0.586
0.576
0.562

Table 2: Word error rates when training and testing on the
Fisher and CALLHOME test sets, of 213 minutes (39,978
words) and 106 minutes (18,792 words) respectively.
fault values of 1 for both parameters. Though values of λ at 4
began to degrade, the improvements were still significantly better than the ASR 1-best WER with these improvements robust
for all hyperparameter combinations evaluated. With no strong
motivation to deviate from the defaults, we left these parametersat λ = 1 and α = 1 and evaluated on the test sets.
During tuning, we also evaluated the parameterizations dis|e)cussed in 5. The best parameterization, P P0 (f, was used0f P (f |e)for the subsequent evaluation. Permitting null alignments in thetranslation model WFST reduced performance for all parametervariations, most notably P (e|f ). The model results presented
permit no null alignments.
6.3. Results and discussion
Table 2 shows the results of unsupervised learning and evaluation across both of the test sets, with the 1-best TM outperforming the ASR baseline, but underperforming Lattice TM on bothtest sets. Figure 4 illustrates the change in performance whenthe models are restricted by being trained on subsets of the test
sets. Training data is scaled up towards the natural unsupervised case where the training data comprises all the lattice data
to be decoded. When training data is limited, the translation
model trained on the 1-best path adversely affects performance,increasing the WER. In contrast, Lattice TM remains robust.
An example (used in Figure 1) from the CALLHOME test
set epitomizes why the TM learnt from the lattice outperforms
that learnt from the 1-best path. The English translation is‘aha, but one never knows’ and the gold transcription is ‘Ahá,
pero una nunca sabe’.4 The better path is the bottom one,choosing ‘nunca’ over ‘son’. However, the 1-best path chooses‘son’. Training a translation model from the erroneous 1-best
path causes negative reinforcement, where the TM is even more
likely to assign a high probability to ‘son’ given ‘never’, and in
the absence of sufficient training data, this has significant effect.
Since ‘never’ and ‘nunca’ are relatively frequently occurring in the test data, the 1-best TM actually assigns a reasonable
4 The gold transcription is notably unobtainable from the lattice. Reduced pruning of lattices are likely to further improve scores.

CALLHOME test set

0.355
0.350
0.345
0.340
0.335
0.330

0.58
0.57
0.56
0.55
0

10 20 30 40

0

5

10

15

20

# hours of test+training set used for TM training
ASR 1-best;

1-best TM;

Lattice TM

Figure 5: Word error rates when training data is scaled up to
many hours.
lexical translation score to this pair, however this is not enoughto overcome the lattice’s bias and the reasonable probability of‘son’ given ‘never’ learnt from the erroneous transcription.
It is also worth considering how the performance of thesemethods scale up to more data. Figure 5 presents evaluations
on the same test sets, but permitting more training data from therespective corpora. It is interesting to note that Lattice TM continues to outperform the 1-best TM approach. This suggests that
Lattice TM gains an advantage from the extra information encoded in the lattice beyond avoiding the negative reinforcementof the 1-best TM approach.
This method is very fast, with composition, sampling andcaching for 1,000 utterances taking between 3 and 4 seconds
on a single 1.80GHz Intel i7-4500U core. Running on the 213
minute Fisher evaluation set (Table 2) took less than 5 minutes,
and scales roughly linearly with more training data.

7. Conclusion and future work
We have demonstrated that having a written translation in another language can help improve speech recognition even whenno pre-trained translation model is available. This is achievedby training a translation model directly on the ASR word lattices paired with the written translation, in order to make themost of all information available in the lattice.
One natural setting for such an approach is for computeraided translation of a small language for which there exists written data but no parallel corpora with the larger target language.
However, since most languages have inadequate or no ASR
technology and stand to gain the most from improved speech
recognition systems, future work should also strive to reverse
the role of the languages in this setup, addressing the speechof a small language paired with a written translation in a larger
language. Such bilingual data can be collected using a tool suchas Aikuma [28]. For this to work, an ASR system with a lexicon
and language model needs to be trained (using a tool such as
Woefzela [29]), or sidestep this need by working directly on the
speech signal or phoneme lattices. There also exist many languages which have limited but detailed parallel data and comprehensive linguistic description. Such approaches may prove
useful there by bootstrapping a translation model with the available glosses and improving ASR of the language with the methods described in this paper.
Finally, it is likely this work could all be extended to copewith interpreted speech using lattices on both the source andtarget sides, though increased computational complexity must
be addressed along with the nuances associated with interpreted
speech [20].

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